Project 2 Predicting Salary
Adam E.
2024-04-21
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## cov, smooth, varKey question for this study: Is it possible to predict when someone is likely to make over 50k?
- Secondary question: What variables best predict a subject making over 50k?
Data Descriptions:
Variable Name Role Type Demographic Description Units Missing Values
age Feature Integer Age N/A no
workclass Feature Categorical Income Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. yes
fnlwgt Feature Integer no
education Feature Categorical Education Level Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. no
education-num Feature Integer Education Level no
marital-status Feature Categorical Other Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. no
occupation Feature Categorical Other Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. yes
relationship Feature Categorical Other Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. no
race Feature Categorical Race White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. no
sex Feature Binary Sex Female, Male. no
capital-gain Feature Integer no
capital-loss Feature Integer no
hours-per-week Feature Integer no
native-country Feature Categorical Other [1] “” ” ?” ” Cambodia” ” Canada”
[5] ” China” ” Columbia” ” Cuba” ” Dominican-Republic”
[9] ” Ecuador” ” El-Salvador” ” England” ” France”
[13] ” Germany” ” Greece” ” Guatemala” ” Haiti”
[17] ” Holand-Netherlands” ” Honduras” ” Hong” ” Hungary”
[21] ” India” ” Iran” ” Ireland” ” Italy”
[25] ” Jamaica” ” Japan” ” Laos” ” Mexico”
[29] ” Nicaragua” ” Outlying-US(Guam-USVI-etc)” ” Peru” ” Philippines”
[33] ” Poland” ” Portugal” ” Puerto-Rico” ” Scotland”
[37] ” South” ” Taiwan” ” Thailand” ” Trinadad&Tobago”
[41] ” United-States” ” Vietnam” ” Yugoslavia” yes income Target Binary Income >50K, <=50K. no
Note: * fnlwgt (third column) - change to ‘similar_pop_count’ represents the number of subjects in the target population that the responding subject represents. * education_num - change to ‘years_eduction’ - count of total years in school * sex - changed to ‘gender’
## age workclass similar_pop_count education_level years_education
## 1 39 State-gov 77516 Bachelors 13
## 2 50 Self-emp-not-inc 83311 Bachelors 13
## 3 38 Private 215646 HS-grad 9
## 4 53 Private 234721 11th 7
## 5 28 Private 338409 Bachelors 13
## 6 37 Private 284582 Masters 14
## 7 49 Private 160187 9th 5
## 8 52 Self-emp-not-inc 209642 HS-grad 9
## 9 31 Private 45781 Masters 14
## 10 42 Private 159449 Bachelors 13
## 11 37 Private 280464 Some-college 10
## 12 30 State-gov 141297 Bachelors 13
## 13 23 Private 122272 Bachelors 13
## 14 32 Private 205019 Assoc-acdm 12
## 15 40 Private 121772 Assoc-voc 11
## 16 34 Private 245487 7th-8th 4
## 17 25 Self-emp-not-inc 176756 HS-grad 9
## 18 32 Private 186824 HS-grad 9
## 19 38 Private 28887 11th 7
## 20 43 Self-emp-not-inc 292175 Masters 14
## 21 40 Private 193524 Doctorate 16
## 22 54 Private 302146 HS-grad 9
## 23 35 Federal-gov 76845 9th 5
## 24 43 Private 117037 11th 7
## 25 59 Private 109015 HS-grad 9
## 26 56 Local-gov 216851 Bachelors 13
## 27 19 Private 168294 HS-grad 9
## 28 54 ? 180211 Some-college 10
## 29 39 Private 367260 HS-grad 9
## 30 49 Private 193366 HS-grad 9
## 31 23 Local-gov 190709 Assoc-acdm 12
## 32 20 Private 266015 Some-college 10
## 33 45 Private 386940 Bachelors 13
## 34 30 Federal-gov 59951 Some-college 10
## 35 22 State-gov 311512 Some-college 10
## 36 48 Private 242406 11th 7
## 37 21 Private 197200 Some-college 10
## 38 19 Private 544091 HS-grad 9
## 39 31 Private 84154 Some-college 10
## 40 48 Self-emp-not-inc 265477 Assoc-acdm 12
## 41 31 Private 507875 9th 5
## 42 53 Self-emp-not-inc 88506 Bachelors 13
## 43 24 Private 172987 Bachelors 13
## 44 49 Private 94638 HS-grad 9
## 45 25 Private 289980 HS-grad 9
## 46 57 Federal-gov 337895 Bachelors 13
## 47 53 Private 144361 HS-grad 9
## 48 44 Private 128354 Masters 14
## 49 41 State-gov 101603 Assoc-voc 11
## 50 29 Private 271466 Assoc-voc 11
## 51 25 Private 32275 Some-college 10
## 52 18 Private 226956 HS-grad 9
## 53 47 Private 51835 Prof-school 15
## 54 50 Federal-gov 251585 Bachelors 13
## 55 47 Self-emp-inc 109832 HS-grad 9
## 56 43 Private 237993 Some-college 10
## 57 46 Private 216666 5th-6th 3
## 58 35 Private 56352 Assoc-voc 11
## 59 41 Private 147372 HS-grad 9
## 60 30 Private 188146 HS-grad 9
## 61 30 Private 59496 Bachelors 13
## 62 32 ? 293936 7th-8th 4
## 63 48 Private 149640 HS-grad 9
## 64 42 Private 116632 Doctorate 16
## 65 29 Private 105598 Some-college 10
## 66 36 Private 155537 HS-grad 9
## 67 28 Private 183175 Some-college 10
## 68 53 Private 169846 HS-grad 9
## 69 49 Self-emp-inc 191681 Some-college 10
## 70 25 ? 200681 Some-college 10
## 71 19 Private 101509 Some-college 10
## 72 31 Private 309974 Bachelors 13
## 73 29 Self-emp-not-inc 162298 Bachelors 13
## 74 23 Private 211678 Some-college 10
## 75 79 Private 124744 Some-college 10
## 76 27 Private 213921 HS-grad 9
## 77 40 Private 32214 Assoc-acdm 12
## 78 67 ? 212759 10th 6
## 79 18 Private 309634 11th 7
## 80 31 Local-gov 125927 7th-8th 4
## 81 18 Private 446839 HS-grad 9
## 82 52 Private 276515 Bachelors 13
## 83 46 Private 51618 HS-grad 9
## 84 59 Private 159937 HS-grad 9
## 85 44 Private 343591 HS-grad 9
## 86 53 Private 346253 HS-grad 9
## 87 49 Local-gov 268234 HS-grad 9
## 88 33 Private 202051 Masters 14
## 89 30 Private 54334 9th 5
## 90 43 Federal-gov 410867 Doctorate 16
## 91 57 Private 249977 Assoc-voc 11
## 92 37 Private 286730 Some-college 10
## 93 28 Private 212563 Some-college 10
## 94 30 Private 117747 HS-grad 9
## 95 34 Local-gov 226296 Bachelors 13
## 96 29 Local-gov 115585 Some-college 10
## 97 48 Self-emp-not-inc 191277 Doctorate 16
## 98 37 Private 202683 Some-college 10
## 99 48 Private 171095 Assoc-acdm 12
## 100 32 Federal-gov 249409 HS-grad 9
## 101 76 Private 124191 Masters 14
## 102 44 Private 198282 Bachelors 13
## 103 47 Self-emp-not-inc 149116 Masters 14
## 104 20 Private 188300 Some-college 10
## 105 29 Private 103432 HS-grad 9
## 106 32 Self-emp-inc 317660 HS-grad 9
## 107 17 ? 304873 10th 6
## 108 30 Private 194901 11th 7
## 109 31 Local-gov 189265 HS-grad 9
## 110 42 Private 124692 HS-grad 9
## 111 24 Private 432376 Bachelors 13
## 112 38 Private 65324 Prof-school 15
## 113 56 Self-emp-not-inc 335605 HS-grad 9
## 114 28 Private 377869 Some-college 10
## 115 36 Private 102864 HS-grad 9
## 116 53 Private 95647 9th 5
## 117 56 Self-emp-inc 303090 Some-college 10
## 118 49 Local-gov 197371 Assoc-voc 11
## 119 55 Private 247552 Some-college 10
## 120 22 Private 102632 HS-grad 9
## 121 21 Private 199915 Some-college 10
## 122 40 Private 118853 Bachelors 13
## 123 30 Private 77143 Bachelors 13
## 124 29 State-gov 267989 Bachelors 13
## 125 19 Private 301606 Some-college 10
## 126 47 Private 287828 Bachelors 13
## 127 20 Private 111697 Some-college 10
## 128 31 Private 114937 Assoc-acdm 12
## 129 35 ? 129305 HS-grad 9
## 130 39 Private 365739 Some-college 10
## 131 28 Private 69621 Assoc-acdm 12
## 132 24 Private 43323 HS-grad 9
## 133 38 Self-emp-not-inc 120985 HS-grad 9
## 134 37 Private 254202 Bachelors 13
## 135 46 Private 146195 Assoc-acdm 12
## 136 38 Federal-gov 125933 Masters 14
## 137 43 Self-emp-not-inc 56920 HS-grad 9
## 138 27 Private 163127 Assoc-voc 11
## 139 20 Private 34310 Some-college 10
## 140 49 Private 81973 Some-college 10
## 141 61 Self-emp-inc 66614 HS-grad 9
## 142 27 Private 232782 Some-college 10
## 143 19 Private 316868 Some-college 10
## 144 45 Private 196584 Assoc-voc 11
## 145 70 Private 105376 Some-college 10
## 146 31 Private 185814 HS-grad 9
## 147 22 Private 175374 Some-college 10
## 148 36 Private 108293 HS-grad 9
## 149 64 Private 181232 11th 7
## 150 43 ? 174662 Some-college 10
## 151 47 Local-gov 186009 Some-college 10
## 152 34 Private 198183 HS-grad 9
## 153 33 Private 163003 Bachelors 13
## 154 21 Private 296158 HS-grad 9
## 155 52 ? 252903 HS-grad 9
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## 157 23 Private 214542 Bachelors 13
## 158 71 Self-emp-not-inc 494223 Some-college 10
## 159 29 Private 191535 HS-grad 9
## 160 42 Private 228456 Bachelors 13
## 161 68 ? 38317 1st-4th 2
## 162 25 Private 252752 HS-grad 9
## 163 44 Self-emp-inc 78374 Masters 14
## 164 28 Private 88419 HS-grad 9
## 165 45 Self-emp-not-inc 201080 Masters 14
## 166 36 Private 207157 Some-college 10
## 167 39 Federal-gov 235485 Assoc-acdm 12
## 168 46 State-gov 102628 Masters 14
## 169 18 Private 25828 11th 7
## 170 66 Local-gov 54826 Assoc-voc 11
## 171 27 Private 124953 HS-grad 9
## 172 28 State-gov 175325 HS-grad 9
## 173 51 Private 96062 Some-college 10
## 174 27 Private 428030 Bachelors 13
## 175 28 State-gov 149624 Bachelors 13
## 176 27 Private 253814 HS-grad 9
## 177 21 Private 312956 HS-grad 9
## 178 34 Private 483777 HS-grad 9
## 179 18 Private 183930 HS-grad 9
## 180 33 Private 37274 Bachelors 13
## 181 44 Local-gov 181344 Some-college 10
## 182 43 Private 114580 Some-college 10
## 183 30 Private 633742 Some-college 10
## 184 40 Private 286370 7th-8th 4
## 185 37 Federal-gov 29054 Some-college 10
## 186 34 Private 304030 HS-grad 9
## 187 41 Self-emp-not-inc 143129 Bachelors 13
## 188 53 ? 135105 Bachelors 13
## 189 31 Private 99928 Masters 14
## 190 58 State-gov 109567 Doctorate 16
## 191 38 Private 155222 Some-college 10
## 192 24 Private 159567 Some-college 10
## 193 41 Local-gov 523910 Bachelors 13
## 194 47 Private 120939 Some-college 10
## 195 41 Federal-gov 130760 Bachelors 13
## 196 23 Private 197387 5th-6th 3
## 197 36 Private 99374 Some-college 10
## 198 40 Federal-gov 56795 Masters 14
## 199 35 Private 138992 Masters 14
## 200 24 Self-emp-not-inc 32921 HS-grad 9
## 201 26 Private 397317 Masters 14
## 202 19 ? 170653 HS-grad 9
## 203 51 Private 259323 Bachelors 13
## 204 42 Local-gov 254817 Some-college 10
## 205 37 State-gov 48211 HS-grad 9
## 206 18 Private 140164 11th 7
## 207 36 Private 128757 Bachelors 13
## 208 35 Private 36270 HS-grad 9
## 209 58 Self-emp-inc 210563 HS-grad 9
## 210 17 Private 65368 11th 7
## 211 44 Local-gov 160943 HS-grad 9
## 212 37 Private 208358 HS-grad 9
## 213 35 Private 153790 Some-college 10
## 214 60 Private 85815 HS-grad 9
## 215 54 Self-emp-inc 125417 7th-8th 4
## 216 37 Private 635913 Bachelors 13
## 217 50 Private 313321 Assoc-acdm 12
## 218 38 Private 182609 Bachelors 13
## 219 45 Private 109434 Bachelors 13
## 220 25 Private 255004 10th 6
## 221 31 Private 197860 Some-college 10
## 222 64 ? 187656 1st-4th 2
## 223 90 Private 51744 HS-grad 9
## 224 54 Private 176681 HS-grad 9
## 225 53 Local-gov 140359 Preschool 1
## 226 18 Private 243313 HS-grad 9
## 227 60 ? 24215 10th 6
## 228 66 Self-emp-not-inc 167687 HS-grad 9
## 229 75 Private 314209 Assoc-voc 11
## 230 65 Private 176796 HS-grad 9
## 231 35 Private 538583 11th 7
## 232 41 Private 130408 HS-grad 9
## 233 25 Private 159732 Some-college 10
## 234 33 Private 110978 Some-college 10
## 235 28 Private 76714 Prof-school 15
## 236 59 State-gov 268700 HS-grad 9
## 237 40 State-gov 170525 Some-college 10
## 238 41 Private 180138 Bachelors 13
## 239 38 Local-gov 115076 Masters 14
## 240 23 Private 115458 HS-grad 9
## 241 40 Private 347890 Bachelors 13
## 242 41 Self-emp-not-inc 196001 HS-grad 9
## 243 24 State-gov 273905 Assoc-acdm 12
## 244 20 ? 119156 Some-college 10
## 245 38 Private 179488 Some-college 10
## 246 56 Private 203580 HS-grad 9
## 247 58 Private 236596 HS-grad 9
## 248 32 Private 183916 HS-grad 9
## 249 40 Private 207578 Assoc-acdm 12
## 250 45 Private 153141 HS-grad 9
## 251 41 Private 112763 Prof-school 15
## 252 42 Private 390781 Bachelors 13
## 253 59 Local-gov 171328 10th 6
## 254 19 Local-gov 27382 Some-college 10
## 255 58 Private 259014 Some-college 10
## 256 42 Self-emp-not-inc 303044 HS-grad 9
## 257 20 Private 117789 HS-grad 9
## 258 32 Private 172579 HS-grad 9
## 259 45 Private 187666 Assoc-voc 11
## 260 50 Private 204518 7th-8th 4
## 261 36 Private 150042 Bachelors 13
## 262 45 Private 98092 HS-grad 9
## 263 17 Private 245918 11th 7
## 264 59 Private 146013 Some-college 10
## 265 26 Private 378322 11th 7
## 266 37 Self-emp-inc 257295 Some-college 10
## 267 19 ? 218956 Some-college 10
## 268 64 Private 21174 HS-grad 9
## 269 33 Private 185480 Bachelors 13
## 270 33 Private 222205 HS-grad 9
## 271 61 Private 69867 HS-grad 9
## 272 17 Private 191260 9th 5
## 273 50 Self-emp-not-inc 30653 Masters 14
## 274 27 Local-gov 209109 Masters 14
## 275 30 Private 70377 HS-grad 9
## 276 43 Private 477983 HS-grad 9
## 277 44 Private 170924 Some-college 10
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## 280 24 Private 279472 Some-college 10
## 281 22 Private 34918 Bachelors 13
## 282 42 Local-gov 97688 Some-college 10
## 283 34 Private 175413 Assoc-acdm 12
## 284 60 Private 173960 Bachelors 13
## 285 21 Private 205759 HS-grad 9
## 286 57 Federal-gov 425161 Masters 14
## 287 41 Private 220531 Prof-school 15
## 288 50 Private 176609 Some-college 10
## 289 25 Private 371987 Bachelors 13
## 290 50 Private 193884 7th-8th 4
## 291 36 Private 200352 Bachelors 13
## 292 31 Private 127595 HS-grad 9
## 293 29 Local-gov 220419 Bachelors 13
## 294 21 Private 231931 Some-college 10
## 295 27 Private 248402 Bachelors 13
## 296 65 Private 111095 HS-grad 9
## 297 37 Self-emp-inc 57424 Bachelors 13
## 298 39 ? 157443 Masters 14
## 299 24 Private 278130 HS-grad 9
## 300 38 Private 169469 HS-grad 9
## 301 48 Private 146268 Bachelors 13
## 302 21 Private 153718 Some-college 10
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## 305 24 Private 303296 Some-college 10
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## 307 26 Private 193945 Assoc-acdm 12
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## 310 41 Self-emp-inc 34987 Some-college 10
## 311 26 Private 59306 Bachelors 13
## 312 34 Private 142897 Masters 14
## 313 19 ? 860348 Some-college 10
## 314 36 Self-emp-not-inc 205607 Bachelors 13
## 315 22 Private 199698 Some-college 10
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## 317 77 Self-emp-not-inc 138714 Some-college 10
## 318 22 Private 399087 5th-6th 3
## 319 29 Private 423158 Some-college 10
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## 324 29 Private 200381 11th 7
## 325 76 Self-emp-not-inc 174309 Masters 14
## 326 63 Self-emp-not-inc 78383 HS-grad 9
## 327 23 ? 211601 Assoc-voc 11
## 328 43 Private 187728 Some-college 10
## 329 58 Self-emp-not-inc 321171 HS-grad 9
## 330 66 Private 127921 HS-grad 9
## 331 41 Private 206565 Some-college 10
## 332 26 Private 224563 Bachelors 13
## 333 47 Private 178686 Assoc-voc 11
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## 341 26 Private 94936 Assoc-acdm 12
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## 350 31 Private 156464 Bachelors 13
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## 12 Married-civ-spouse Prof-specialty Husband
## 13 Never-married Adm-clerical Own-child
## 14 Never-married Sales Not-in-family
## 15 Married-civ-spouse Craft-repair Husband
## 16 Married-civ-spouse Transport-moving Husband
## 17 Never-married Farming-fishing Own-child
## 18 Never-married Machine-op-inspct Unmarried
## 19 Married-civ-spouse Sales Husband
## 20 Divorced Exec-managerial Unmarried
## 21 Married-civ-spouse Prof-specialty Husband
## 22 Separated Other-service Unmarried
## 23 Married-civ-spouse Farming-fishing Husband
## 24 Married-civ-spouse Transport-moving Husband
## 25 Divorced Tech-support Unmarried
## 26 Married-civ-spouse Tech-support Husband
## 27 Never-married Craft-repair Own-child
## 28 Married-civ-spouse ? Husband
## 29 Divorced Exec-managerial Not-in-family
## 30 Married-civ-spouse Craft-repair Husband
## 31 Never-married Protective-serv Not-in-family
## 32 Never-married Sales Own-child
## 33 Divorced Exec-managerial Own-child
## 34 Married-civ-spouse Adm-clerical Own-child
## 35 Married-civ-spouse Other-service Husband
## 36 Never-married Machine-op-inspct Unmarried
## 37 Never-married Machine-op-inspct Own-child
## 38 Married-AF-spouse Adm-clerical Wife
## 39 Married-civ-spouse Sales Husband
## 40 Married-civ-spouse Prof-specialty Husband
## 41 Married-civ-spouse Machine-op-inspct Husband
## 42 Married-civ-spouse Prof-specialty Husband
## 43 Married-civ-spouse Tech-support Husband
## 44 Separated Adm-clerical Unmarried
## 45 Never-married Handlers-cleaners Not-in-family
## 46 Married-civ-spouse Prof-specialty Husband
## 47 Married-civ-spouse Machine-op-inspct Husband
## 48 Divorced Exec-managerial Unmarried
## 49 Married-civ-spouse Craft-repair Husband
## 50 Never-married Prof-specialty Not-in-family
## 51 Married-civ-spouse Exec-managerial Wife
## 52 Never-married Other-service Own-child
## 53 Married-civ-spouse Prof-specialty Wife
## 54 Divorced Exec-managerial Not-in-family
## 55 Divorced Exec-managerial Not-in-family
## 56 Married-civ-spouse Tech-support Husband
## 57 Married-civ-spouse Machine-op-inspct Husband
## 58 Married-civ-spouse Other-service Husband
## 59 Married-civ-spouse Adm-clerical Husband
## 60 Married-civ-spouse Machine-op-inspct Husband
## 61 Married-civ-spouse Sales Husband
## 62 Married-spouse-absent ? Not-in-family
## 63 Married-civ-spouse Transport-moving Husband
## 64 Married-civ-spouse Prof-specialty Husband
## 65 Divorced Tech-support Not-in-family
## 66 Married-civ-spouse Craft-repair Husband
## 67 Divorced Adm-clerical Not-in-family
## 68 Married-civ-spouse Adm-clerical Wife
## 69 Married-civ-spouse Exec-managerial Husband
## 70 Never-married ? Own-child
## 71 Never-married Prof-specialty Own-child
## 72 Separated Sales Own-child
## 73 Married-civ-spouse Sales Husband
## 74 Never-married Machine-op-inspct Not-in-family
## 75 Married-civ-spouse Prof-specialty Other-relative
## 76 Never-married Other-service Own-child
## 77 Married-civ-spouse Adm-clerical Husband
## 78 Married-civ-spouse ? Husband
## 79 Never-married Other-service Own-child
## 80 Married-civ-spouse Farming-fishing Husband
## 81 Never-married Sales Not-in-family
## 82 Married-civ-spouse Other-service Husband
## 83 Married-civ-spouse Other-service Wife
## 84 Married-civ-spouse Sales Husband
## 85 Divorced Craft-repair Not-in-family
## 86 Divorced Sales Own-child
## 87 Married-civ-spouse Protective-serv Husband
## 88 Married-civ-spouse Prof-specialty Husband
## 89 Never-married Sales Not-in-family
## 90 Never-married Prof-specialty Not-in-family
## 91 Married-civ-spouse Prof-specialty Husband
## 92 Divorced Craft-repair Unmarried
## 93 Divorced Machine-op-inspct Unmarried
## 94 Married-civ-spouse Sales Wife
## 95 Married-civ-spouse Protective-serv Husband
## 96 Never-married Handlers-cleaners Not-in-family
## 97 Married-civ-spouse Prof-specialty Husband
## 98 Married-civ-spouse Sales Husband
## 99 Divorced Exec-managerial Unmarried
## 100 Never-married Other-service Own-child
## 101 Married-civ-spouse Exec-managerial Husband
## 102 Married-civ-spouse Exec-managerial Husband
## 103 Never-married Prof-specialty Not-in-family
## 104 Never-married Tech-support Own-child
## 105 Never-married Craft-repair Not-in-family
## 106 Married-civ-spouse Craft-repair Husband
## 107 Never-married ? Own-child
## 108 Never-married Handlers-cleaners Own-child
## 109 Never-married Adm-clerical Not-in-family
## 110 Married-civ-spouse Handlers-cleaners Husband
## 111 Never-married Sales Other-relative
## 112 Married-civ-spouse Prof-specialty Husband
## 113 Married-civ-spouse Other-service Husband
## 114 Married-civ-spouse Sales Wife
## 115 Never-married Machine-op-inspct Own-child
## 116 Married-civ-spouse Handlers-cleaners Husband
## 117 Married-civ-spouse Sales Husband
## 118 Married-civ-spouse Craft-repair Husband
## 119 Married-civ-spouse Sales Husband
## 120 Never-married Craft-repair Not-in-family
## 121 Never-married Other-service Own-child
## 122 Married-civ-spouse Exec-managerial Husband
## 123 Never-married Exec-managerial Own-child
## 124 Married-civ-spouse Prof-specialty Husband
## 125 Never-married Other-service Own-child
## 126 Married-civ-spouse Exec-managerial Wife
## 127 Never-married Adm-clerical Own-child
## 128 Married-civ-spouse Adm-clerical Husband
## 129 Married-civ-spouse ? Husband
## 130 Divorced Craft-repair Not-in-family
## 131 Never-married Sales Not-in-family
## 132 Never-married Other-service Not-in-family
## 133 Married-civ-spouse Craft-repair Husband
## 134 Married-civ-spouse Sales Husband
## 135 Divorced Tech-support Not-in-family
## 136 Married-civ-spouse Prof-specialty Husband
## 137 Married-civ-spouse Craft-repair Husband
## 138 Married-civ-spouse Adm-clerical Wife
## 139 Never-married Sales Own-child
## 140 Married-civ-spouse Craft-repair Husband
## 141 Married-civ-spouse Craft-repair Husband
## 142 Never-married Sales Own-child
## 143 Never-married Other-service Own-child
## 144 Never-married Prof-specialty Not-in-family
## 145 Never-married Tech-support Other-relative
## 146 Never-married Transport-moving Unmarried
## 147 Married-civ-spouse Other-service Husband
## 148 Widowed Other-service Unmarried
## 149 Married-civ-spouse Craft-repair Husband
## 150 Divorced ? Not-in-family
## 151 Divorced Adm-clerical Unmarried
## 152 Never-married Adm-clerical Not-in-family
## 153 Never-married Exec-managerial Other-relative
## 154 Never-married Craft-repair Own-child
## 155 Divorced ? Not-in-family
## 156 Married-civ-spouse Craft-repair Husband
## 157 Never-married Handlers-cleaners Not-in-family
## 158 Separated Sales Unmarried
## 159 Divorced Craft-repair Not-in-family
## 160 Separated Other-service Other-relative
## 161 Divorced ? Not-in-family
## 162 Never-married Other-service Unmarried
## 163 Divorced Exec-managerial Unmarried
## 164 Never-married Exec-managerial Not-in-family
## 165 Married-civ-spouse Sales Husband
## 166 Divorced Other-service Unmarried
## 167 Never-married Exec-managerial Not-in-family
## 168 Widowed Protective-serv Unmarried
## 169 Never-married Handlers-cleaners Own-child
## 170 Widowed Prof-specialty Not-in-family
## 171 Never-married Other-service Not-in-family
## 172 Married-civ-spouse Protective-serv Husband
## 173 Married-civ-spouse Sales Husband
## 174 Never-married Craft-repair Not-in-family
## 175 Married-civ-spouse Prof-specialty Husband
## 176 Married-spouse-absent Sales Unmarried
## 177 Never-married Craft-repair Own-child
## 178 Never-married Handlers-cleaners Not-in-family
## 179 Never-married Other-service Own-child
## 180 Married-civ-spouse Prof-specialty Husband
## 181 Married-civ-spouse Exec-managerial Husband
## 182 Divorced Adm-clerical Not-in-family
## 183 Never-married Craft-repair Not-in-family
## 184 Married-civ-spouse Machine-op-inspct Husband
## 185 Married-civ-spouse Adm-clerical Husband
## 186 Married-civ-spouse Adm-clerical Husband
## 187 Divorced Exec-managerial Not-in-family
## 188 Divorced ? Not-in-family
## 189 Married-civ-spouse Prof-specialty Wife
## 190 Married-civ-spouse Prof-specialty Husband
## 191 Divorced Machine-op-inspct Not-in-family
## 192 Married-civ-spouse Machine-op-inspct Husband
## 193 Married-civ-spouse Craft-repair Husband
## 194 Married-civ-spouse Tech-support Husband
## 195 Married-civ-spouse Tech-support Husband
## 196 Married-civ-spouse Transport-moving Other-relative
## 197 Divorced Craft-repair Not-in-family
## 198 Never-married Exec-managerial Not-in-family
## 199 Married-civ-spouse Prof-specialty Other-relative
## 200 Never-married Sales Not-in-family
## 201 Never-married Prof-specialty Not-in-family
## 202 Never-married ? Own-child
## 203 Married-civ-spouse Exec-managerial Husband
## 204 Never-married Prof-specialty Not-in-family
## 205 Divorced Adm-clerical Unmarried
## 206 Never-married Sales Own-child
## 207 Married-civ-spouse Other-service Husband
## 208 Divorced Craft-repair Not-in-family
## 209 Married-civ-spouse Sales Wife
## 210 Never-married Sales Own-child
## 211 Married-civ-spouse Transport-moving Husband
## 212 Married-civ-spouse Craft-repair Husband
## 213 Never-married Sales Not-in-family
## 214 Married-civ-spouse Craft-repair Husband
## 215 Married-civ-spouse Machine-op-inspct Husband
## 216 Never-married Exec-managerial Not-in-family
## 217 Divorced Sales Not-in-family
## 218 Married-civ-spouse Sales Husband
## 219 Married-civ-spouse Prof-specialty Husband
## 220 Never-married Craft-repair Not-in-family
## 221 Married-civ-spouse Handlers-cleaners Husband
## 222 Divorced ? Not-in-family
## 223 Never-married Other-service Not-in-family
## 224 Married-civ-spouse Adm-clerical Husband
## 225 Never-married Machine-op-inspct Not-in-family
## 226 Never-married Sales Own-child
## 227 Divorced ? Not-in-family
## 228 Married-civ-spouse Farming-fishing Husband
## 229 Widowed Adm-clerical Not-in-family
## 230 Divorced Adm-clerical Not-in-family
## 231 Separated Transport-moving Not-in-family
## 232 Divorced Sales Unmarried
## 233 Never-married Adm-clerical Not-in-family
## 234 Divorced Craft-repair Other-relative
## 235 Never-married Prof-specialty Not-in-family
## 236 Married-civ-spouse Other-service Husband
## 237 Never-married Adm-clerical Not-in-family
## 238 Married-civ-spouse Exec-managerial Husband
## 239 Married-civ-spouse Exec-managerial Husband
## 240 Never-married Transport-moving Own-child
## 241 Married-civ-spouse Prof-specialty Husband
## 242 Married-civ-spouse Other-service Wife
## 243 Married-civ-spouse Protective-serv Husband
## 244 Never-married ? Own-child
## 245 Divorced Craft-repair Not-in-family
## 246 Married-civ-spouse Adm-clerical Husband
## 247 Married-civ-spouse Adm-clerical Husband
## 248 Never-married Other-service Not-in-family
## 249 Married-civ-spouse Tech-support Husband
## 250 Married-civ-spouse Adm-clerical Husband
## 251 Married-civ-spouse Prof-specialty Wife
## 252 Married-civ-spouse Adm-clerical Wife
## 253 Widowed Other-service Unmarried
## 254 Never-married Adm-clerical Own-child
## 255 Never-married Transport-moving Not-in-family
## 256 Married-civ-spouse Farming-fishing Husband
## 257 Never-married Other-service Own-child
## 258 Separated Other-service Not-in-family
## 259 Widowed Exec-managerial Not-in-family
## 260 Divorced Craft-repair Not-in-family
## 261 Divorced Prof-specialty Own-child
## 262 Married-civ-spouse Sales Husband
## 263 Never-married Other-service Own-child
## 264 Married-civ-spouse Sales Husband
## 265 Married-civ-spouse Craft-repair Husband
## 266 Married-civ-spouse Exec-managerial Husband
## 267 Never-married ? Own-child
## 268 Married-civ-spouse Exec-managerial Husband
## 269 Never-married Prof-specialty Not-in-family
## 270 Married-civ-spouse Craft-repair Wife
## 271 Married-civ-spouse Exec-managerial Husband
## 272 Never-married Other-service Own-child
## 273 Married-civ-spouse Farming-fishing Husband
## 274 Never-married Prof-specialty Own-child
## 275 Divorced Prof-specialty Own-child
## 276 Married-civ-spouse Handlers-cleaners Husband
## 277 Married-civ-spouse Craft-repair Husband
## 278 Never-married Exec-managerial Not-in-family
## 279 Never-married Tech-support Own-child
## 280 Married-civ-spouse Machine-op-inspct Wife
## 281 Never-married Prof-specialty Not-in-family
## 282 Married-civ-spouse Craft-repair Husband
## 283 Divorced Sales Unmarried
## 284 Divorced Prof-specialty Not-in-family
## 285 Never-married Handlers-cleaners Own-child
## 286 Married-civ-spouse Sales Husband
## 287 Married-civ-spouse Prof-specialty Husband
## 288 Divorced Other-service Not-in-family
## 289 Never-married Exec-managerial Not-in-family
## 290 Married-civ-spouse Craft-repair Husband
## 291 Married-civ-spouse Prof-specialty Husband
## 292 Divorced Prof-specialty Not-in-family
## 293 Never-married Protective-serv Not-in-family
## 294 Never-married Sales Own-child
## 295 Never-married Tech-support Unmarried
## 296 Married-civ-spouse Transport-moving Husband
## 297 Divorced Sales Not-in-family
## 298 Married-civ-spouse ? Wife
## 299 Never-married Craft-repair Own-child
## 300 Divorced Sales Not-in-family
## 301 Married-civ-spouse Adm-clerical Husband
## 302 Never-married Other-service Not-in-family
## 303 Married-civ-spouse Transport-moving Husband
## 304 Married-civ-spouse Sales Husband
## 305 Married-civ-spouse Adm-clerical Wife
## 306 Divorced Adm-clerical Not-in-family
## 307 Never-married Tech-support Not-in-family
## 308 Never-married Prof-specialty Not-in-family
## 309 Married-civ-spouse Adm-clerical Husband
## 310 Married-civ-spouse Farming-fishing Husband
## 311 Never-married Sales Not-in-family
## 312 Married-civ-spouse Exec-managerial Husband
## 313 Never-married ? Own-child
## 314 Divorced Prof-specialty Not-in-family
## 315 Never-married Sales Own-child
## 316 Never-married Machine-op-inspct Not-in-family
## 317 Married-civ-spouse Sales Husband
## 318 Married-civ-spouse Machine-op-inspct Other-relative
## 319 Never-married Tech-support Not-in-family
## 320 Widowed Other-service Not-in-family
## 321 Married-civ-spouse Exec-managerial Husband
## 322 Married-civ-spouse Craft-repair Husband
## 323 Divorced Transport-moving Not-in-family
## 324 Never-married Exec-managerial Not-in-family
## 325 Married-civ-spouse Craft-repair Husband
## 326 Married-civ-spouse Farming-fishing Husband
## 327 Never-married ? Own-child
## 328 Married-civ-spouse Prof-specialty Wife
## 329 Married-civ-spouse Handlers-cleaners Husband
## 330 Never-married Transport-moving Not-in-family
## 331 Never-married Craft-repair Not-in-family
## 332 Never-married Adm-clerical Not-in-family
## 333 Never-married Other-service Not-in-family
## 334 Married-civ-spouse Adm-clerical Husband
## 335 Married-civ-spouse Transport-moving Husband
## 336 Never-married Other-service Other-relative
## 337 Never-married Craft-repair Other-relative
## 338 Never-married Prof-specialty Not-in-family
## 339 Never-married Sales Unmarried
## 340 Married-civ-spouse Other-service Husband
## 341 Never-married Sales Not-in-family
## 342 Married-civ-spouse Craft-repair Husband
## 343 Married-civ-spouse Protective-serv Husband
## 344 Never-married Farming-fishing Not-in-family
## 345 Never-married Protective-serv Own-child
## 346 Never-married Sales Not-in-family
## 347 Married-civ-spouse ? Husband
## 348 Divorced ? Unmarried
## 349 Married-spouse-absent Other-service Not-in-family
## 350 Never-married Prof-specialty Own-child
## 351 Married-civ-spouse Exec-managerial Husband
## 352 Married-civ-spouse Other-service Husband
## 353 Married-civ-spouse Exec-managerial Husband
## 354 Married-civ-spouse Transport-moving Husband
## 355 Married-civ-spouse ? Husband
## 356 Married-civ-spouse Adm-clerical Husband
## 357 Widowed Other-service Unmarried
## 358 Married-civ-spouse Tech-support Husband
## 359 Married-civ-spouse Adm-clerical Wife
## 360 Married-civ-spouse Craft-repair Husband
## 361 Married-civ-spouse Other-service Wife
## 362 Married-civ-spouse Prof-specialty Husband
## 363 Married-civ-spouse Transport-moving Husband
## 364 Divorced Exec-managerial Not-in-family
## 365 Married-spouse-absent Sales Not-in-family
## 366 Married-civ-spouse Craft-repair Husband
## 367 Married-civ-spouse Sales Husband
## 368 Never-married Sales Not-in-family
## 369 Divorced Exec-managerial Not-in-family
## 370 Divorced Sales Unmarried
## 371 Never-married Adm-clerical Unmarried
## 372 Never-married Other-service Own-child
## 373 Married-civ-spouse Machine-op-inspct Husband
## 374 Married-civ-spouse Machine-op-inspct Husband
## 375 Never-married Craft-repair Not-in-family
## 376 Never-married Adm-clerical Own-child
## 377 Married-civ-spouse Craft-repair Wife
## 378 Never-married Sales Not-in-family
## 379 Divorced Exec-managerial Not-in-family
## 380 Married-civ-spouse Sales Husband
## 381 Divorced Transport-moving Not-in-family
## 382 Divorced Craft-repair Not-in-family
## 383 Married-civ-spouse Craft-repair Husband
## 384 Divorced Sales Not-in-family
## 385 Married-civ-spouse Transport-moving Wife
## 386 Never-married Other-service Not-in-family
## 387 Separated Machine-op-inspct Not-in-family
## 388 Married-civ-spouse Adm-clerical Wife
## 389 Never-married Adm-clerical Own-child
## 390 Never-married Other-service Other-relative
## 391 Married-civ-spouse Other-service Wife
## 392 Never-married Craft-repair Not-in-family
## 393 Separated Craft-repair Not-in-family
## 394 Married-civ-spouse Exec-managerial Husband
## 395 Never-married Other-service Not-in-family
## 396 Married-civ-spouse Tech-support Husband
## 397 Divorced Handlers-cleaners Unmarried
## 398 Never-married ? Other-relative
## 399 Married-civ-spouse Exec-managerial Husband
## 400 Married-civ-spouse Exec-managerial Husband
## 401 Never-married Handlers-cleaners Own-child
## 402 Never-married Other-service Not-in-family
## 403 Married-civ-spouse Protective-serv Husband
## 404 Divorced Tech-support Not-in-family
## 405 Married-civ-spouse Handlers-cleaners Husband
## 406 Married-civ-spouse Exec-managerial Husband
## 407 Married-civ-spouse Adm-clerical Husband
## 408 Married-civ-spouse Craft-repair Husband
## 409 Married-civ-spouse ? Wife
## 410 Married-civ-spouse Exec-managerial Husband
## 411 Divorced Other-service Not-in-family
## 412 Never-married Handlers-cleaners Unmarried
## 413 Divorced Exec-managerial Unmarried
## 414 Married-civ-spouse Exec-managerial Husband
## 415 Married-civ-spouse Machine-op-inspct Husband
## 416 Divorced Craft-repair Not-in-family
## 417 Married-civ-spouse Machine-op-inspct Husband
## 418 Never-married Other-service Own-child
## 419 Separated Machine-op-inspct Not-in-family
## 420 Divorced Transport-moving Unmarried
## 421 Married-civ-spouse Sales Husband
## 422 Never-married Handlers-cleaners Own-child
## 423 Married-civ-spouse Exec-managerial Wife
## 424 Divorced Farming-fishing Not-in-family
## 425 Never-married Craft-repair Unmarried
## 426 Married-civ-spouse Transport-moving Husband
## 427 Never-married Prof-specialty Not-in-family
## 428 Never-married Handlers-cleaners Not-in-family
## 429 Married-civ-spouse Exec-managerial Husband
## 430 Married-civ-spouse Prof-specialty Husband
## 431 Widowed ? Not-in-family
## 432 Never-married ? Own-child
## 433 Married-spouse-absent Prof-specialty Not-in-family
## 434 Married-civ-spouse Other-service Wife
## 435 Never-married Craft-repair Own-child
## 436 Married-civ-spouse Prof-specialty Husband
## 437 Never-married Farming-fishing Not-in-family
## 438 Never-married Protective-serv Not-in-family
## 439 Married-civ-spouse Adm-clerical Husband
## 440 Married-civ-spouse Adm-clerical Wife
## 441 Never-married Prof-specialty Not-in-family
## 442 Divorced Adm-clerical Not-in-family
## 443 Never-married Armed-Forces Own-child
## 444 Married-civ-spouse Prof-specialty Husband
## 445 Never-married Craft-repair Other-relative
## 446 Never-married Sales Not-in-family
## 447 Widowed Exec-managerial Unmarried
## 448 Married-civ-spouse Craft-repair Husband
## 449 Married-civ-spouse Machine-op-inspct Husband
## 450 Never-married ? Own-child
## 451 Never-married Machine-op-inspct Own-child
## 452 Married-civ-spouse Craft-repair Husband
## 453 Married-civ-spouse Craft-repair Husband
## 454 Married-civ-spouse Exec-managerial Husband
## 455 Married-civ-spouse Sales Wife
## 456 Married-civ-spouse Machine-op-inspct Husband
## 457 Married-civ-spouse Exec-managerial Wife
## 458 Married-civ-spouse Transport-moving Husband
## 459 Married-civ-spouse Transport-moving Husband
## 460 Married-civ-spouse ? Husband
## 461 Never-married Farming-fishing Own-child
## 462 Married-civ-spouse Transport-moving Husband
## 463 Married-civ-spouse Prof-specialty Husband
## 464 Married-spouse-absent Craft-repair Unmarried
## 465 Never-married Tech-support Not-in-family
## 466 Married-civ-spouse Adm-clerical Wife
## 467 Divorced Transport-moving Unmarried
## 468 Married-civ-spouse Transport-moving Husband
## 469 Married-civ-spouse Exec-managerial Husband
## 470 Married-civ-spouse Tech-support Wife
## 471 Never-married Prof-specialty Unmarried
## 472 Never-married ? Own-child
## 473 Never-married Craft-repair Not-in-family
## 474 Never-married Transport-moving Not-in-family
## 475 Divorced Other-service Not-in-family
## 476 Never-married Craft-repair Not-in-family
## 477 Never-married Other-service Not-in-family
## 478 Never-married Handlers-cleaners Own-child
## 479 Never-married Other-service Not-in-family
## 480 Never-married Other-service Unmarried
## 481 Married-civ-spouse Craft-repair Husband
## 482 Married-civ-spouse Craft-repair Husband
## 483 Never-married Prof-specialty Not-in-family
## 484 Married-spouse-absent Craft-repair Unmarried
## 485 Never-married ? Other-relative
## 486 Married-spouse-absent Adm-clerical Own-child
## 487 Married-civ-spouse ? Husband
## 488 Married-civ-spouse Transport-moving Husband
## 489 Divorced Prof-specialty Unmarried
## 490 Married-civ-spouse Sales Husband
## 491 Never-married Other-service Not-in-family
## 492 Divorced Adm-clerical Not-in-family
## 493 Never-married Exec-managerial Not-in-family
## 494 Divorced Prof-specialty Unmarried
## 495 Never-married Craft-repair Own-child
## 496 Divorced Adm-clerical Unmarried
## 497 Never-married Machine-op-inspct Own-child
## 498 Never-married Other-service Own-child
## 499 Never-married Prof-specialty Not-in-family
## 500 Married-civ-spouse ? Husband
## 501 Never-married Farming-fishing Not-in-family
## 502 Married-civ-spouse Transport-moving Husband
## 503 Married-civ-spouse Adm-clerical Husband
## 504 Divorced Other-service Unmarried
## 505 Never-married Sales Not-in-family
## 506 Never-married Exec-managerial Not-in-family
## 507 Never-married Protective-serv Own-child
## 508 Married-civ-spouse Exec-managerial Husband
## 509 Separated Adm-clerical Unmarried
## 510 Divorced Other-service Unmarried
## 511 Married-spouse-absent Handlers-cleaners Unmarried
## 512 Widowed ? Unmarried
## 513 Never-married Craft-repair Not-in-family
## 514 Married-civ-spouse Prof-specialty Husband
## 515 Divorced Craft-repair Not-in-family
## 516 Widowed ? Unmarried
## 517 Never-married Adm-clerical Not-in-family
## 518 Never-married ? Own-child
## 519 Never-married ? Unmarried
## 520 Married-civ-spouse Prof-specialty Husband
## 521 Married-civ-spouse Exec-managerial Husband
## 522 Married-civ-spouse Exec-managerial Husband
## 523 Divorced Handlers-cleaners Not-in-family
## 524 Married-civ-spouse Tech-support Husband
## 525 Divorced Machine-op-inspct Unmarried
## 526 Married-civ-spouse Sales Wife
## 527 Married-civ-spouse Exec-managerial Husband
## 528 Divorced Adm-clerical Not-in-family
## 529 Married-civ-spouse Craft-repair Husband
## 530 Married-civ-spouse Prof-specialty Husband
## 531 Divorced Machine-op-inspct Unmarried
## 532 Never-married Machine-op-inspct Not-in-family
## 533 Married-civ-spouse Other-service Husband
## 534 Married-civ-spouse Exec-managerial Wife
## 535 Married-civ-spouse Prof-specialty Husband
## 536 Married-civ-spouse Tech-support Husband
## 537 Widowed Priv-house-serv Not-in-family
## 538 Separated Craft-repair Not-in-family
## 539 Never-married Farming-fishing Not-in-family
## 540 Never-married ? Unmarried
## 541 Married-civ-spouse Transport-moving Husband
## 542 Never-married Prof-specialty Own-child
## 543 Never-married Handlers-cleaners Unmarried
## 544 Never-married Transport-moving Not-in-family
## 545 Married-civ-spouse Sales Husband
## 546 Never-married Handlers-cleaners Not-in-family
## 547 Married-civ-spouse Other-service Wife
## 548 Married-civ-spouse Protective-serv Husband
## 549 Never-married Handlers-cleaners Own-child
## 550 Never-married Craft-repair Unmarried
## 551 Divorced Exec-managerial Not-in-family
## 552 Separated Other-service Unmarried
## 553 Widowed Adm-clerical Unmarried
## 554 Married-civ-spouse Machine-op-inspct Wife
## 555 Divorced Craft-repair Unmarried
## 556 Never-married Machine-op-inspct Not-in-family
## 557 Married-civ-spouse Machine-op-inspct Husband
## 558 Separated Adm-clerical Unmarried
## 559 Married-civ-spouse Tech-support Wife
## 560 Married-civ-spouse Other-service Husband
## 561 Never-married Transport-moving Not-in-family
## 562 Married-civ-spouse Sales Husband
## 563 Married-civ-spouse Prof-specialty Husband
## 564 Never-married Handlers-cleaners Unmarried
## 565 Married-civ-spouse Prof-specialty Husband
## 566 Never-married Handlers-cleaners Own-child
## 567 Married-civ-spouse Exec-managerial Husband
## 568 Separated Other-service Unmarried
## 569 Separated Craft-repair Not-in-family
## 570 Divorced Exec-managerial Unmarried
## 571 Never-married Craft-repair Own-child
## 572 Married-civ-spouse Prof-specialty Husband
## 573 Never-married Sales Own-child
## 574 Married-civ-spouse Craft-repair Husband
## 575 Never-married Prof-specialty Not-in-family
## 576 Married-civ-spouse Exec-managerial Wife
## 577 Never-married ? Own-child
## 578 Divorced Sales Unmarried
## 579 Never-married Prof-specialty Not-in-family
## 580 Married-civ-spouse Craft-repair Husband
## 581 Married-civ-spouse ? Husband
## 582 Married-civ-spouse Sales Husband
## 583 Married-civ-spouse Craft-repair Husband
## 584 Married-civ-spouse Machine-op-inspct Husband
## 585 Never-married Tech-support Not-in-family
## 586 Never-married Sales Own-child
## 587 Never-married Adm-clerical Not-in-family
## 588 Never-married Other-service Own-child
## 589 Married-civ-spouse Craft-repair Husband
## 590 Divorced Prof-specialty Not-in-family
## 591 Married-civ-spouse ? Husband
## 592 Married-civ-spouse ? Wife
## 593 Never-married Transport-moving Not-in-family
## 594 Married-civ-spouse Prof-specialty Husband
## 595 Never-married Adm-clerical Own-child
## 596 Never-married Sales Own-child
## 597 Never-married ? Unmarried
## 598 Married-civ-spouse Prof-specialty Husband
## 599 Never-married Tech-support Not-in-family
## 600 Never-married Prof-specialty Not-in-family
## 601 Never-married Tech-support Not-in-family
## 602 Never-married Adm-clerical Own-child
## 603 Divorced Other-service Not-in-family
## 604 Married-civ-spouse Other-service Husband
## 605 Never-married Farming-fishing Not-in-family
## 606 Never-married Adm-clerical Unmarried
## 607 Married-civ-spouse Craft-repair Husband
## 608 Never-married Craft-repair Not-in-family
## 609 Separated Exec-managerial Not-in-family
## 610 Never-married Other-service Own-child
## 611 Divorced Prof-specialty Not-in-family
## 612 Never-married Craft-repair Not-in-family
## 613 Married-civ-spouse Handlers-cleaners Husband
## 614 Never-married Prof-specialty Own-child
## 615 Never-married Other-service Own-child
## 616 Never-married Handlers-cleaners Own-child
## 617 Married-civ-spouse Transport-moving Husband
## 618 Married-civ-spouse Exec-managerial Husband
## 619 Married-civ-spouse Sales Husband
## 620 Married-civ-spouse Prof-specialty Husband
## 621 Never-married Sales Own-child
## 622 Widowed Other-service Not-in-family
## 623 Widowed Priv-house-serv Unmarried
## 624 Married-civ-spouse Transport-moving Husband
## 625 Married-spouse-absent Handlers-cleaners Unmarried
## 626 Never-married Farming-fishing Own-child
## 627 Married-civ-spouse Sales Husband
## 628 Married-civ-spouse Sales Husband
## 629 Married-civ-spouse Exec-managerial Husband
## 630 Married-civ-spouse Sales Husband
## 631 Never-married Other-service Unmarried
## 632 Separated Other-service Unmarried
## 633 Married-civ-spouse Craft-repair Husband
## 634 Married-civ-spouse Adm-clerical Wife
## 635 Married-civ-spouse Craft-repair Husband
## 636 Never-married Other-service Own-child
## 637 Never-married Handlers-cleaners Not-in-family
## 638 Married-civ-spouse Exec-managerial Husband
## 639 Married-civ-spouse Transport-moving Husband
## 640 Married-civ-spouse Prof-specialty Husband
## 641 Divorced Craft-repair Not-in-family
## 642 Married-civ-spouse Craft-repair Husband
## 643 Never-married Handlers-cleaners Own-child
## 644 Married-civ-spouse Sales Husband
## 645 Married-civ-spouse Craft-repair Husband
## 646 Separated Prof-specialty Not-in-family
## 647 Divorced Adm-clerical Unmarried
## 648 Married-civ-spouse Craft-repair Husband
## 649 Married-civ-spouse ? Husband
## 650 Married-civ-spouse Tech-support Husband
## 651 Never-married Other-service Own-child
## 652 Never-married Exec-managerial Other-relative
## 653 Married-civ-spouse Prof-specialty Husband
## 654 Separated Exec-managerial Not-in-family
## 655 Never-married Machine-op-inspct Own-child
## 656 Never-married Adm-clerical Unmarried
## 657 Widowed ? Unmarried
## 658 Married-civ-spouse Craft-repair Husband
## 659 Married-civ-spouse Craft-repair Husband
## 660 Married-civ-spouse Machine-op-inspct Husband
## 661 Married-civ-spouse Handlers-cleaners Husband
## 662 Never-married Sales Not-in-family
## 663 Never-married Prof-specialty Not-in-family
## 664 Never-married Prof-specialty Not-in-family
## 665 Never-married Sales Not-in-family
## 666 Divorced Prof-specialty Unmarried
## 667 Married-civ-spouse Transport-moving Husband
## 668 Married-civ-spouse Craft-repair Husband
## 669 Married-spouse-absent ? Own-child
## 670 Never-married Machine-op-inspct Not-in-family
## 671 Divorced Exec-managerial Not-in-family
## 672 Never-married ? Own-child
## 673 Never-married Farming-fishing Own-child
## 674 Married-civ-spouse Exec-managerial Husband
## 675 Married-civ-spouse Sales Husband
## 676 Never-married Sales Own-child
## 677 Never-married Other-service Own-child
## 678 Married-civ-spouse Adm-clerical Husband
## 679 Never-married Craft-repair Not-in-family
## 680 Widowed Exec-managerial Not-in-family
## 681 Never-married Protective-serv Own-child
## 682 Married-civ-spouse Craft-repair Husband
## 683 Married-civ-spouse Craft-repair Husband
## 684 Separated Exec-managerial Unmarried
## 685 Never-married Adm-clerical Own-child
## 686 Divorced Exec-managerial Not-in-family
## 687 Never-married ? Not-in-family
## 688 Married-civ-spouse Prof-specialty Wife
## 689 Never-married Exec-managerial Not-in-family
## 690 Never-married Other-service Other-relative
## 691 Married-civ-spouse Farming-fishing Husband
## 692 Never-married Machine-op-inspct Own-child
## 693 Married-civ-spouse Prof-specialty Husband
## 694 Never-married Prof-specialty Own-child
## 695 Never-married Tech-support Not-in-family
## 696 Never-married ? Other-relative
## 697 Never-married Prof-specialty Not-in-family
## 698 Married-civ-spouse Tech-support Husband
## 699 Never-married Prof-specialty Own-child
## 700 Never-married Sales Own-child
## 701 Never-married Prof-specialty Not-in-family
## 702 Widowed Sales Unmarried
## 703 Never-married Handlers-cleaners Own-child
## 704 Married-civ-spouse Craft-repair Husband
## 705 Divorced Prof-specialty Unmarried
## 706 Divorced Exec-managerial Own-child
## 707 Married-civ-spouse Other-service Husband
## 708 Never-married Sales Not-in-family
## 709 Married-civ-spouse Craft-repair Husband
## 710 Never-married Other-service Other-relative
## 711 Married-civ-spouse Prof-specialty Husband
## 712 Divorced Adm-clerical Unmarried
## 713 Separated Prof-specialty Not-in-family
## 714 Married-civ-spouse Tech-support Husband
## 715 Divorced Adm-clerical Unmarried
## 716 Separated Other-service Unmarried
## 717 Married-civ-spouse Machine-op-inspct Husband
## 718 Never-married Tech-support Own-child
## 719 Never-married Sales Own-child
## 720 Married-civ-spouse Exec-managerial Husband
## 721 Never-married Other-service Not-in-family
## 722 Married-civ-spouse Craft-repair Husband
## 723 Never-married Prof-specialty Not-in-family
## 724 Married-civ-spouse Exec-managerial Husband
## 725 Married-civ-spouse Exec-managerial Husband
## 726 Never-married Prof-specialty Not-in-family
## 727 Married-civ-spouse Sales Wife
## 728 Never-married Other-service Not-in-family
## 729 Married-civ-spouse Other-service Husband
## 730 Married-civ-spouse Exec-managerial Husband
## 731 Never-married Other-service Not-in-family
## 732 Divorced Adm-clerical Not-in-family
## 733 Married-civ-spouse Adm-clerical Husband
## 734 Never-married Other-service Own-child
## 735 Married-civ-spouse ? Wife
## 736 Married-civ-spouse Craft-repair Husband
## 737 Never-married Sales Not-in-family
## 738 Married-civ-spouse Prof-specialty Husband
## 739 Widowed Other-service Unmarried
## 740 Married-civ-spouse Tech-support Husband
## 741 Never-married Other-service Other-relative
## 742 Married-civ-spouse Transport-moving Husband
## 743 Never-married Exec-managerial Not-in-family
## 744 Never-married Tech-support Own-child
## 745 Never-married Handlers-cleaners Own-child
## 746 Married-civ-spouse Prof-specialty Wife
## 747 Married-civ-spouse Transport-moving Husband
## 748 Never-married Adm-clerical Own-child
## 749 Never-married Craft-repair Own-child
## 750 Married-civ-spouse Prof-specialty Husband
## 751 Married-civ-spouse Exec-managerial Husband
## 752 Never-married Prof-specialty Unmarried
## 753 Married-civ-spouse Craft-repair Husband
## 754 Never-married Transport-moving Unmarried
## 755 Never-married Handlers-cleaners Not-in-family
## 756 Never-married Craft-repair Own-child
## 757 Married-civ-spouse Transport-moving Husband
## 758 Never-married Prof-specialty Not-in-family
## 759 Married-civ-spouse Adm-clerical Wife
## 760 Never-married Farming-fishing Not-in-family
## 761 Never-married Other-service Not-in-family
## 762 Married-civ-spouse Adm-clerical Wife
## 763 Divorced Protective-serv Unmarried
## 764 Married-civ-spouse Exec-managerial Husband
## 765 Never-married Sales Not-in-family
## 766 Separated Other-service Unmarried
## 767 Married-spouse-absent Other-service Other-relative
## 768 Married-civ-spouse Sales Wife
## 769 Divorced Sales Not-in-family
## 770 Never-married Machine-op-inspct Not-in-family
## 771 Never-married Farming-fishing Not-in-family
## 772 Separated Farming-fishing Not-in-family
## 773 Divorced Craft-repair Unmarried
## 774 Married-civ-spouse Tech-support Wife
## 775 Never-married Sales Not-in-family
## 776 Divorced Tech-support Not-in-family
## 777 Divorced Adm-clerical Unmarried
## 778 Married-civ-spouse Prof-specialty Husband
## 779 Married-civ-spouse Sales Husband
## 780 Never-married Tech-support Not-in-family
## 781 Married-civ-spouse Craft-repair Husband
## 782 Never-married Machine-op-inspct Not-in-family
## 783 Married-civ-spouse Exec-managerial Husband
## 784 Married-civ-spouse Exec-managerial Husband
## 785 Never-married Sales Not-in-family
## 786 Divorced Adm-clerical Not-in-family
## 787 Married-civ-spouse Farming-fishing Husband
## 788 Never-married Machine-op-inspct Own-child
## 789 Married-civ-spouse ? Husband
## 790 Never-married ? Not-in-family
## 791 Married-civ-spouse Tech-support Husband
## 792 Married-civ-spouse Tech-support Husband
## 793 Never-married ? Own-child
## 794 Never-married Sales Not-in-family
## 795 Married-civ-spouse Transport-moving Husband
## 796 Never-married Other-service Other-relative
## 797 Married-civ-spouse Machine-op-inspct Husband
## 798 Never-married Machine-op-inspct Not-in-family
## 799 Never-married Tech-support Not-in-family
## 800 Married-civ-spouse Exec-managerial Husband
## 801 Divorced Craft-repair Not-in-family
## 802 Never-married Other-service Own-child
## 803 Never-married Farming-fishing Unmarried
## 804 Divorced Adm-clerical Unmarried
## 805 Never-married Sales Own-child
## 806 Married-civ-spouse Craft-repair Husband
## 807 Married-spouse-absent ? Other-relative
## 808 Married-civ-spouse Prof-specialty Husband
## 809 Married-civ-spouse Prof-specialty Husband
## 810 Never-married Transport-moving Own-child
## 811 Separated Other-service Own-child
## 812 Never-married ? Own-child
## 813 Divorced Exec-managerial Not-in-family
## 814 Never-married Adm-clerical Own-child
## 815 Never-married Farming-fishing Own-child
## 816 Married-civ-spouse Other-service Wife
## 817 Never-married Prof-specialty Not-in-family
## 818 Married-civ-spouse Craft-repair Husband
## 819 Married-civ-spouse Transport-moving Husband
## 820 Married-civ-spouse Transport-moving Husband
## 821 Never-married Adm-clerical Unmarried
## 822 Never-married Prof-specialty Not-in-family
## 823 Never-married Tech-support Own-child
## 824 Widowed Adm-clerical Not-in-family
## 825 Married-civ-spouse Exec-managerial Husband
## 826 Widowed Transport-moving Unmarried
## 827 Widowed Other-service Not-in-family
## 828 Separated Machine-op-inspct Unmarried
## 829 Never-married Adm-clerical Not-in-family
## 830 Never-married Sales Own-child
## 831 Married-civ-spouse ? Husband
## 832 Married-civ-spouse Adm-clerical Wife
## 833 Never-married Handlers-cleaners Own-child
## 834 Married-civ-spouse Exec-managerial Husband
## 835 Widowed ? Not-in-family
## 836 Married-civ-spouse Machine-op-inspct Husband
## 837 Divorced Adm-clerical Not-in-family
## 838 Divorced Prof-specialty Not-in-family
## 839 Never-married Exec-managerial Not-in-family
## 840 Widowed Farming-fishing Not-in-family
## 841 Never-married Sales Own-child
## 842 Married-civ-spouse Craft-repair Husband
## 843 Married-civ-spouse Prof-specialty Husband
## 844 Widowed Prof-specialty Not-in-family
## 845 Married-civ-spouse Craft-repair Husband
## 846 Divorced Prof-specialty Not-in-family
## 847 Divorced Other-service Not-in-family
## 848 Married-civ-spouse Exec-managerial Husband
## 849 Married-civ-spouse Craft-repair Husband
## 850 Never-married Craft-repair Own-child
## 851 Widowed Prof-specialty Not-in-family
## 852 Divorced Adm-clerical Not-in-family
## 853 Married-civ-spouse Sales Husband
## 854 Never-married Prof-specialty Not-in-family
## 855 Divorced Prof-specialty Own-child
## 856 Never-married Transport-moving Own-child
## 857 Never-married Adm-clerical Own-child
## 858 Married-civ-spouse Other-service Husband
## 859 Never-married Prof-specialty Own-child
## 860 Never-married Exec-managerial Not-in-family
## 861 Never-married Adm-clerical Own-child
## 862 Married-civ-spouse Farming-fishing Husband
## 863 Married-civ-spouse Exec-managerial Husband
## 864 Married-civ-spouse Exec-managerial Husband
## 865 Married-civ-spouse Machine-op-inspct Husband
## 866 Never-married Sales Not-in-family
## 867 Never-married Handlers-cleaners Not-in-family
## 868 Never-married Sales Own-child
## 869 Married-civ-spouse Farming-fishing Husband
## 870 Separated Prof-specialty Own-child
## 871 Married-civ-spouse Exec-managerial Husband
## 872 Never-married Transport-moving Own-child
## 873 Married-civ-spouse Sales Husband
## 874 Married-civ-spouse Sales Husband
## 875 Divorced Other-service Unmarried
## 876 Married-civ-spouse Exec-managerial Husband
## 877 Never-married Handlers-cleaners Unmarried
## 878 Divorced Adm-clerical Unmarried
## 879 Married-civ-spouse Transport-moving Husband
## 880 Never-married Adm-clerical Not-in-family
## 881 Divorced Machine-op-inspct Not-in-family
## 882 Married-civ-spouse Adm-clerical Other-relative
## 883 Married-civ-spouse Craft-repair Husband
## 884 Married-civ-spouse Sales Husband
## 885 Married-civ-spouse Craft-repair Wife
## 886 Married-civ-spouse ? Husband
## 887 Never-married Exec-managerial Own-child
## 888 Married-civ-spouse Craft-repair Husband
## 889 Married-civ-spouse Sales Husband
## 890 Divorced Tech-support Not-in-family
## 891 Divorced ? Unmarried
## 892 Never-married Exec-managerial Not-in-family
## 893 Married-civ-spouse Adm-clerical Wife
## 894 Married-civ-spouse Prof-specialty Husband
## 895 Married-civ-spouse Farming-fishing Husband
## 896 Married-civ-spouse Prof-specialty Husband
## 897 Divorced Sales Own-child
## 898 Never-married Exec-managerial Own-child
## 899 Married-civ-spouse Exec-managerial Husband
## 900 Married-civ-spouse Tech-support Husband
## 901 Married-civ-spouse Other-service Husband
## 902 Married-civ-spouse Craft-repair Husband
## 903 Never-married Handlers-cleaners Own-child
## 904 Married-civ-spouse ? Own-child
## 905 Never-married Sales Not-in-family
## 906 Divorced Machine-op-inspct Not-in-family
## 907 Divorced Machine-op-inspct Not-in-family
## 908 Never-married Prof-specialty Own-child
## 909 Divorced Handlers-cleaners Own-child
## 910 Married-civ-spouse Tech-support Husband
## 911 Never-married Sales Not-in-family
## 912 Divorced Craft-repair Not-in-family
## 913 Married-civ-spouse Farming-fishing Husband
## 914 Never-married Craft-repair Not-in-family
## 915 Never-married Sales Own-child
## 916 Married-civ-spouse Exec-managerial Husband
## 917 Married-civ-spouse Prof-specialty Wife
## 918 Married-civ-spouse Exec-managerial Husband
## 919 Married-civ-spouse Exec-managerial Husband
## 920 Never-married Other-service Own-child
## 921 Never-married Sales Not-in-family
## 922 Never-married Transport-moving Unmarried
## 923 Married-civ-spouse Machine-op-inspct Wife
## 924 Married-civ-spouse Adm-clerical Wife
## 925 Married-civ-spouse ? Husband
## 926 Married-civ-spouse Machine-op-inspct Husband
## 927 Married-civ-spouse Machine-op-inspct Husband
## 928 Married-civ-spouse Exec-managerial Husband
## 929 Never-married Sales Not-in-family
## 930 Married-spouse-absent Priv-house-serv Not-in-family
## 931 Divorced Machine-op-inspct Not-in-family
## 932 Never-married ? Own-child
## 933 Married-civ-spouse Other-service Husband
## 934 Married-civ-spouse Adm-clerical Wife
## 935 Married-civ-spouse Machine-op-inspct Husband
## 936 Married-civ-spouse Craft-repair Husband
## 937 Never-married Prof-specialty Not-in-family
## 938 Married-civ-spouse Prof-specialty Husband
## 939 Married-civ-spouse Craft-repair Husband
## 940 Never-married Sales Not-in-family
## 941 Divorced Other-service Unmarried
## 942 Married-civ-spouse Machine-op-inspct Husband
## 943 Married-civ-spouse Exec-managerial Husband
## 944 Never-married Other-service Not-in-family
## 945 Married-civ-spouse Machine-op-inspct Husband
## 946 Divorced Exec-managerial Unmarried
## 947 Divorced Adm-clerical Unmarried
## 948 Married-civ-spouse Exec-managerial Wife
## 949 Divorced Craft-repair Unmarried
## 950 Divorced Farming-fishing Unmarried
## 951 Married-civ-spouse Prof-specialty Wife
## 952 Married-civ-spouse Exec-managerial Husband
## 953 Widowed Other-service Unmarried
## 954 Never-married Other-service Own-child
## 955 Married-civ-spouse Exec-managerial Husband
## 956 Married-civ-spouse Craft-repair Husband
## 957 Married-civ-spouse Sales Husband
## 958 Divorced Transport-moving Not-in-family
## 959 Married-civ-spouse Exec-managerial Husband
## 960 Married-civ-spouse Exec-managerial Husband
## 961 Married-civ-spouse Prof-specialty Husband
## 962 Divorced Machine-op-inspct Not-in-family
## 963 Married-civ-spouse Craft-repair Husband
## 964 Divorced Exec-managerial Unmarried
## 965 Married-civ-spouse Transport-moving Husband
## 966 Never-married Other-service Own-child
## 967 Married-civ-spouse Sales Husband
## 968 Married-civ-spouse Craft-repair Husband
## 969 Married-civ-spouse Exec-managerial Husband
## 970 Widowed ? Not-in-family
## 971 Married-civ-spouse Craft-repair Husband
## 972 Never-married Tech-support Not-in-family
## 973 Widowed Exec-managerial Not-in-family
## 974 Married-civ-spouse Craft-repair Husband
## 975 Never-married Transport-moving Unmarried
## 976 Never-married Sales Not-in-family
## 977 Never-married Exec-managerial Not-in-family
## 978 Married-civ-spouse Prof-specialty Husband
## 979 Widowed Other-service Not-in-family
## 980 Divorced Handlers-cleaners Own-child
## 981 Never-married Adm-clerical Not-in-family
## 982 Never-married Adm-clerical Not-in-family
## 983 Never-married ? Unmarried
## 984 Never-married Farming-fishing Unmarried
## 985 Widowed Protective-serv Not-in-family
## 986 Divorced Other-service Not-in-family
## 987 Never-married Craft-repair Own-child
## 988 Divorced Adm-clerical Unmarried
## 989 Divorced Craft-repair Unmarried
## 990 Widowed Prof-specialty Not-in-family
## 991 Never-married Sales Not-in-family
## 992 Married-civ-spouse Machine-op-inspct Husband
## 993 Married-civ-spouse Craft-repair Husband
## 994 Married-civ-spouse Adm-clerical Husband
## 995 Never-married Exec-managerial Not-in-family
## 996 Married-civ-spouse Exec-managerial Husband
## 997 Divorced Prof-specialty Not-in-family
## 998 Divorced Exec-managerial Unmarried
## 999 Married-civ-spouse Machine-op-inspct Husband
## 1000 Married-civ-spouse Exec-managerial Husband
## 1001 Never-married Other-service Own-child
## 1002 Married-civ-spouse Prof-specialty Husband
## 1003 Married-civ-spouse Prof-specialty Wife
## 1004 Never-married Prof-specialty Own-child
## 1005 Never-married Other-service Own-child
## 1006 Married-civ-spouse Adm-clerical Husband
## 1007 Separated Machine-op-inspct Not-in-family
## 1008 Married-civ-spouse Exec-managerial Husband
## 1009 Married-civ-spouse Craft-repair Husband
## 1010 Married-civ-spouse Sales Husband
## 1011 Divorced Machine-op-inspct Not-in-family
## 1012 Never-married Transport-moving Own-child
## 1013 Married-civ-spouse Machine-op-inspct Husband
## 1014 Married-civ-spouse Prof-specialty Husband
## 1015 Married-civ-spouse Prof-specialty Husband
## 1016 Married-civ-spouse Handlers-cleaners Husband
## 1017 Married-civ-spouse Tech-support Wife
## 1018 Widowed Other-service Unmarried
## 1019 Divorced Craft-repair Not-in-family
## 1020 Never-married ? Own-child
## 1021 Never-married Craft-repair Not-in-family
## 1022 Married-civ-spouse Prof-specialty Husband
## 1023 Separated Sales Not-in-family
## 1024 Married-civ-spouse Other-service Husband
## 1025 Never-married Machine-op-inspct Unmarried
## 1026 Married-civ-spouse Other-service Husband
## 1027 Never-married Prof-specialty Not-in-family
## 1028 Married-civ-spouse Exec-managerial Husband
## 1029 Married-civ-spouse Adm-clerical Wife
## 1030 Married-spouse-absent Sales Not-in-family
## 1031 Never-married Adm-clerical Not-in-family
## 1032 Never-married Other-service Own-child
## 1033 Never-married Sales Not-in-family
## 1034 Never-married Other-service Not-in-family
## 1035 Widowed ? Not-in-family
## 1036 Never-married ? Unmarried
## 1037 Married-civ-spouse Exec-managerial Husband
## 1038 Never-married Other-service Not-in-family
## 1039 Divorced Priv-house-serv Unmarried
## 1040 Married-civ-spouse ? Wife
## 1041 Never-married Other-service Not-in-family
## 1042 Never-married Machine-op-inspct Own-child
## 1043 Divorced Adm-clerical Not-in-family
## 1044 Married-civ-spouse Exec-managerial Husband
## 1045 Never-married Tech-support Not-in-family
## 1046 Divorced Prof-specialty Unmarried
## 1047 Never-married ? Not-in-family
## 1048 Married-civ-spouse Other-service Husband
## 1049 Never-married Sales Not-in-family
## 1050 Married-civ-spouse Prof-specialty Husband
## 1051 Married-civ-spouse Machine-op-inspct Husband
## 1052 Married-civ-spouse Sales Husband
## 1053 Married-civ-spouse Adm-clerical Husband
## 1054 Married-civ-spouse Sales Husband
## 1055 Divorced Craft-repair Not-in-family
## 1056 Married-civ-spouse Sales Husband
## 1057 Married-civ-spouse Transport-moving Husband
## 1058 Divorced Craft-repair Not-in-family
## 1059 Married-civ-spouse Prof-specialty Husband
## 1060 Married-civ-spouse Craft-repair Husband
## 1061 Married-civ-spouse Exec-managerial Husband
## 1062 Married-civ-spouse Machine-op-inspct Husband
## 1063 Married-civ-spouse Other-service Husband
## 1064 Separated Other-service Unmarried
## 1065 Married-civ-spouse Sales Husband
## 1066 Never-married Other-service Own-child
## 1067 Married-civ-spouse Prof-specialty Husband
## 1068 Married-civ-spouse Transport-moving Husband
## 1069 Married-civ-spouse Exec-managerial Husband
## 1070 Divorced Tech-support Not-in-family
## 1071 Never-married Prof-specialty Own-child
## 1072 Married-civ-spouse Craft-repair Husband
## 1073 Married-civ-spouse Craft-repair Husband
## 1074 Married-civ-spouse Prof-specialty Wife
## 1075 Never-married Adm-clerical Own-child
## 1076 Never-married Craft-repair Own-child
## 1077 Never-married Sales Not-in-family
## 1078 Never-married Sales Own-child
## 1079 Married-civ-spouse Craft-repair Husband
## 1080 Married-civ-spouse Prof-specialty Wife
## 1081 Separated Prof-specialty Unmarried
## 1082 Divorced Exec-managerial Not-in-family
## 1083 Married-civ-spouse Farming-fishing Husband
## 1084 Married-civ-spouse Protective-serv Husband
## 1085 Married-civ-spouse Adm-clerical Husband
## 1086 Married-civ-spouse Transport-moving Husband
## 1087 Married-civ-spouse Protective-serv Husband
## 1088 Never-married Exec-managerial Not-in-family
## 1089 Married-civ-spouse Exec-managerial Husband
## 1090 Divorced Craft-repair Own-child
## 1091 Married-civ-spouse Prof-specialty Husband
## 1092 Never-married Sales Not-in-family
## 1093 Married-spouse-absent Prof-specialty Not-in-family
## 1094 Divorced Craft-repair Not-in-family
## 1095 Never-married Adm-clerical Own-child
## 1096 Never-married Prof-specialty Own-child
## 1097 Married-civ-spouse Machine-op-inspct Husband
## 1098 Never-married ? Own-child
## 1099 Married-civ-spouse Sales Wife
## 1100 Divorced Prof-specialty Unmarried
## 1101 Married-civ-spouse ? Husband
## 1102 Married-civ-spouse Machine-op-inspct Husband
## 1103 Married-civ-spouse Exec-managerial Husband
## 1104 Married-civ-spouse Exec-managerial Husband
## 1105 Married-civ-spouse Prof-specialty Wife
## 1106 Divorced Adm-clerical Unmarried
## 1107 Never-married Prof-specialty Not-in-family
## 1108 Never-married Handlers-cleaners Own-child
## 1109 Married-civ-spouse Craft-repair Husband
## 1110 Married-civ-spouse Transport-moving Husband
## 1111 Married-civ-spouse Sales Husband
## 1112 Married-civ-spouse Sales Wife
## 1113 Never-married Adm-clerical Not-in-family
## 1114 Married-civ-spouse Sales Wife
## 1115 Divorced Handlers-cleaners Own-child
## 1116 Married-civ-spouse Machine-op-inspct Husband
## 1117 Married-civ-spouse Craft-repair Husband
## 1118 Married-civ-spouse Transport-moving Husband
## 1119 Married-civ-spouse Prof-specialty Wife
## 1120 Married-civ-spouse Prof-specialty Husband
## 1121 Married-civ-spouse Tech-support Husband
## 1122 Divorced Tech-support Not-in-family
## 1123 Married-civ-spouse Exec-managerial Husband
## 1124 Married-civ-spouse Tech-support Husband
## 1125 Divorced Other-service Unmarried
## 1126 Never-married Adm-clerical Own-child
## 1127 Married-civ-spouse Transport-moving Husband
## 1128 Widowed Handlers-cleaners Not-in-family
## 1129 Divorced Other-service Unmarried
## 1130 Married-civ-spouse Prof-specialty Husband
## 1131 Never-married Machine-op-inspct Not-in-family
## 1132 Divorced ? Not-in-family
## 1133 Never-married Other-service Unmarried
## 1134 Never-married Adm-clerical Own-child
## 1135 Divorced ? Unmarried
## 1136 Married-civ-spouse Exec-managerial Husband
## 1137 Married-civ-spouse Exec-managerial Husband
## 1138 Married-civ-spouse Adm-clerical Husband
## 1139 Married-civ-spouse Craft-repair Husband
## 1140 Married-civ-spouse Craft-repair Husband
## 1141 Never-married Other-service Own-child
## 1142 Never-married Sales Not-in-family
## 1143 Widowed Sales Unmarried
## 1144 Never-married Other-service Not-in-family
## 1145 Married-civ-spouse Sales Husband
## 1146 Married-civ-spouse Prof-specialty Husband
## 1147 Never-married Exec-managerial Not-in-family
## 1148 Married-civ-spouse Machine-op-inspct Husband
## 1149 Married-civ-spouse Exec-managerial Husband
## 1150 Married-civ-spouse Exec-managerial Husband
## 1151 Never-married Other-service Own-child
## 1152 Divorced Adm-clerical Unmarried
## 1153 Never-married ? Not-in-family
## 1154 Married-civ-spouse Craft-repair Husband
## 1155 Married-civ-spouse Protective-serv Husband
## 1156 Married-civ-spouse Farming-fishing Husband
## 1157 Married-civ-spouse Exec-managerial Husband
## 1158 Married-civ-spouse Prof-specialty Wife
## 1159 Never-married Transport-moving Unmarried
## 1160 Never-married Handlers-cleaners Own-child
## 1161 Married-civ-spouse Craft-repair Husband
## 1162 Widowed Transport-moving Unmarried
## 1163 Married-civ-spouse Sales Husband
## 1164 Married-civ-spouse Tech-support Husband
## 1165 Married-civ-spouse Prof-specialty Wife
## 1166 Divorced Adm-clerical Not-in-family
## 1167 Married-civ-spouse Adm-clerical Husband
## 1168 Never-married ? Own-child
## 1169 Married-civ-spouse Prof-specialty Husband
## 1170 Divorced Transport-moving Not-in-family
## 1171 Divorced Adm-clerical Not-in-family
## 1172 Divorced Other-service Not-in-family
## 1173 Married-civ-spouse Farming-fishing Not-in-family
## 1174 Never-married Other-service Not-in-family
## 1175 Married-civ-spouse Other-service Husband
## 1176 Married-civ-spouse ? Husband
## 1177 Never-married Adm-clerical Own-child
## 1178 Married-civ-spouse Prof-specialty Husband
## 1179 Never-married Adm-clerical Unmarried
## 1180 Never-married Other-service Not-in-family
## 1181 Married-civ-spouse ? Husband
## 1182 Divorced Exec-managerial Not-in-family
## 1183 Married-civ-spouse Prof-specialty Husband
## 1184 Married-civ-spouse Adm-clerical Husband
## 1185 Married-civ-spouse Craft-repair Husband
## 1186 Married-civ-spouse ? Husband
## 1187 Never-married Exec-managerial Own-child
## 1188 Married-civ-spouse Craft-repair Husband
## 1189 Never-married Adm-clerical Not-in-family
## 1190 Never-married Prof-specialty Own-child
## 1191 Divorced Other-service Not-in-family
## 1192 Married-civ-spouse Craft-repair Husband
## 1193 Married-civ-spouse Transport-moving Husband
## 1194 Married-civ-spouse Tech-support Husband
## 1195 Never-married Other-service Own-child
## 1196 Divorced Prof-specialty Not-in-family
## 1197 Married-civ-spouse Prof-specialty Husband
## 1198 Married-civ-spouse Prof-specialty Husband
## 1199 Never-married Machine-op-inspct Own-child
## 1200 Married-civ-spouse Craft-repair Husband
## 1201 Married-civ-spouse Adm-clerical Husband
## 1202 Married-civ-spouse Prof-specialty Husband
## 1203 Divorced Craft-repair Not-in-family
## 1204 Never-married Other-service Not-in-family
## 1205 Married-civ-spouse Craft-repair Husband
## 1206 Married-civ-spouse Sales Husband
## 1207 Married-civ-spouse Protective-serv Husband
## 1208 Married-civ-spouse Sales Husband
## 1209 Never-married ? Own-child
## 1210 Married-civ-spouse Machine-op-inspct Husband
## 1211 Married-civ-spouse Sales Husband
## 1212 Separated Adm-clerical Own-child
## 1213 Divorced Craft-repair Unmarried
## 1214 Married-civ-spouse Prof-specialty Husband
## 1215 Never-married Other-service Own-child
## 1216 Widowed ? Not-in-family
## 1217 Married-civ-spouse Craft-repair Husband
## 1218 Divorced ? Not-in-family
## 1219 Divorced Exec-managerial Not-in-family
## 1220 Married-civ-spouse Prof-specialty Husband
## 1221 Married-civ-spouse Craft-repair Husband
## 1222 Married-civ-spouse Craft-repair Husband
## 1223 Married-civ-spouse Craft-repair Husband
## 1224 Widowed Adm-clerical Own-child
## 1225 Married-civ-spouse Transport-moving Husband
## 1226 Never-married Adm-clerical Not-in-family
## 1227 Married-civ-spouse Prof-specialty Husband
## 1228 Married-civ-spouse Farming-fishing Husband
## 1229 Divorced Adm-clerical Unmarried
## 1230 Married-civ-spouse Prof-specialty Husband
## 1231 Married-civ-spouse Prof-specialty Husband
## 1232 Divorced Adm-clerical Unmarried
## 1233 Never-married Adm-clerical Not-in-family
## 1234 Married-civ-spouse Craft-repair Husband
## 1235 Separated Other-service Not-in-family
## 1236 Never-married Sales Not-in-family
## 1237 Never-married Sales Not-in-family
## 1238 Married-civ-spouse Sales Other-relative
## 1239 Never-married Other-service Own-child
## 1240 Never-married Adm-clerical Own-child
## 1241 Never-married Prof-specialty Not-in-family
## 1242 Separated Adm-clerical Not-in-family
## 1243 Widowed Exec-managerial Not-in-family
## 1244 Married-civ-spouse Exec-managerial Husband
## 1245 Never-married Other-service Not-in-family
## 1246 Widowed Other-service Not-in-family
## 1247 Married-civ-spouse Prof-specialty Husband
## 1248 Divorced Adm-clerical Not-in-family
## 1249 Married-civ-spouse Adm-clerical Wife
## 1250 Never-married Handlers-cleaners Own-child
## 1251 Married-civ-spouse Exec-managerial Husband
## 1252 Married-civ-spouse Exec-managerial Husband
## 1253 Married-civ-spouse Adm-clerical Husband
## 1254 Divorced Adm-clerical Not-in-family
## 1255 Never-married Transport-moving Not-in-family
## 1256 Married-civ-spouse Craft-repair Husband
## 1257 Never-married Adm-clerical Not-in-family
## 1258 Married-civ-spouse Sales Husband
## 1259 Married-civ-spouse Prof-specialty Husband
## 1260 Separated Adm-clerical Not-in-family
## 1261 Married-civ-spouse Craft-repair Husband
## 1262 Married-civ-spouse Transport-moving Husband
## 1263 Widowed ? Not-in-family
## 1264 Widowed Other-service Not-in-family
## 1265 Never-married Craft-repair Own-child
## 1266 Never-married Sales Not-in-family
## 1267 Married-civ-spouse Prof-specialty Husband
## 1268 Married-civ-spouse Craft-repair Husband
## 1269 Married-civ-spouse Adm-clerical Husband
## 1270 Divorced Protective-serv Not-in-family
## 1271 Never-married Exec-managerial Unmarried
## 1272 Married-civ-spouse Other-service Husband
## 1273 Never-married Other-service Not-in-family
## 1274 Never-married Tech-support Not-in-family
## 1275 Never-married Other-service Unmarried
## 1276 Married-civ-spouse Adm-clerical Husband
## 1277 Married-civ-spouse Sales Husband
## 1278 Married-civ-spouse Protective-serv Husband
## 1279 Married-civ-spouse Transport-moving Husband
## 1280 Married-civ-spouse Sales Husband
## 1281 Never-married Sales Not-in-family
## 1282 Divorced Prof-specialty Unmarried
## 1283 Married-civ-spouse ? Husband
## 1284 Never-married Farming-fishing Own-child
## 1285 Married-civ-spouse Adm-clerical Wife
## 1286 Married-civ-spouse Transport-moving Husband
## 1287 Married-civ-spouse Sales Husband
## 1288 Married-civ-spouse Other-service Husband
## 1289 Never-married Exec-managerial Not-in-family
## 1290 Married-civ-spouse Craft-repair Husband
## 1291 Married-civ-spouse ? Husband
## 1292 Widowed Other-service Unmarried
## 1293 Married-civ-spouse Transport-moving Husband
## 1294 Married-civ-spouse Prof-specialty Husband
## 1295 Married-civ-spouse Handlers-cleaners Husband
## 1296 Married-civ-spouse Craft-repair Husband
## 1297 Never-married Other-service Not-in-family
## 1298 Married-civ-spouse Tech-support Husband
## 1299 Never-married Other-service Own-child
## 1300 Married-civ-spouse Craft-repair Husband
## 1301 Never-married Armed-Forces Not-in-family
## 1302 Never-married Other-service Unmarried
## 1303 Married-civ-spouse Transport-moving Wife
## 1304 Married-civ-spouse Prof-specialty Husband
## 1305 Married-civ-spouse Exec-managerial Husband
## 1306 Married-civ-spouse Machine-op-inspct Husband
## 1307 Never-married Machine-op-inspct Not-in-family
## 1308 Never-married Adm-clerical Own-child
## 1309 Married-civ-spouse Sales Husband
## 1310 Married-civ-spouse Craft-repair Wife
## 1311 Married-civ-spouse Exec-managerial Husband
## 1312 Never-married Prof-specialty Not-in-family
## 1313 Never-married ? Not-in-family
## 1314 Divorced Adm-clerical Unmarried
## 1315 Married-civ-spouse Handlers-cleaners Husband
## 1316 Never-married Machine-op-inspct Own-child
## 1317 Married-civ-spouse Prof-specialty Husband
## 1318 Never-married ? Not-in-family
## 1319 Married-civ-spouse Prof-specialty Husband
## 1320 Married-civ-spouse Craft-repair Husband
## 1321 Widowed Adm-clerical Unmarried
## 1322 Never-married Adm-clerical Own-child
## 1323 Never-married Sales Not-in-family
## 1324 Married-civ-spouse Transport-moving Husband
## 1325 Married-civ-spouse Adm-clerical Husband
## 1326 Never-married ? Own-child
## 1327 Married-civ-spouse Handlers-cleaners Husband
## 1328 Widowed Other-service Unmarried
## 1329 Never-married Sales Own-child
## 1330 Never-married Other-service Unmarried
## 1331 Never-married Machine-op-inspct Other-relative
## 1332 Never-married Machine-op-inspct Not-in-family
## 1333 Never-married Machine-op-inspct Not-in-family
## 1334 Married-civ-spouse Prof-specialty Husband
## 1335 Married-civ-spouse Prof-specialty Husband
## 1336 Never-married Prof-specialty Not-in-family
## 1337 Never-married Prof-specialty Not-in-family
## 1338 Married-civ-spouse Exec-managerial Husband
## 1339 Divorced Tech-support Own-child
## 1340 Married-civ-spouse Exec-managerial Husband
## 1341 Never-married Prof-specialty Unmarried
## 1342 Married-spouse-absent Prof-specialty Not-in-family
## 1343 Divorced Adm-clerical Not-in-family
## 1344 Never-married Adm-clerical Own-child
## 1345 Never-married Sales Not-in-family
## 1346 Never-married Adm-clerical Own-child
## 1347 Married-civ-spouse Exec-managerial Husband
## 1348 Never-married ? Own-child
## 1349 Married-civ-spouse Exec-managerial Husband
## 1350 Married-civ-spouse ? Husband
## 1351 Never-married Prof-specialty Not-in-family
## 1352 Married-civ-spouse Craft-repair Husband
## 1353 Separated Sales Own-child
## 1354 Married-civ-spouse Craft-repair Husband
## 1355 Divorced Machine-op-inspct Not-in-family
## 1356 Married-civ-spouse Farming-fishing Husband
## 1357 Never-married Craft-repair Other-relative
## 1358 Never-married Tech-support Not-in-family
## 1359 Never-married Sales Not-in-family
## 1360 Never-married Machine-op-inspct Own-child
## 1361 Never-married Craft-repair Not-in-family
## 1362 Married-civ-spouse Craft-repair Husband
## 1363 Never-married Other-service Not-in-family
## 1364 Divorced Sales Unmarried
## 1365 Never-married Exec-managerial Not-in-family
## 1366 Divorced Exec-managerial Unmarried
## 1367 Married-civ-spouse Machine-op-inspct Wife
## 1368 Widowed Adm-clerical Not-in-family
## 1369 Married-civ-spouse Exec-managerial Husband
## 1370 Never-married Handlers-cleaners Own-child
## 1371 Never-married Other-service Own-child
## 1372 Married-civ-spouse ? Husband
## 1373 Never-married Other-service Not-in-family
## 1374 Married-civ-spouse Sales Husband
## 1375 Never-married Adm-clerical Not-in-family
## 1376 Divorced Sales Unmarried
## 1377 Married-civ-spouse Transport-moving Husband
## 1378 Married-civ-spouse Farming-fishing Husband
## 1379 Married-civ-spouse Sales Husband
## 1380 Divorced Exec-managerial Unmarried
## 1381 Separated Adm-clerical Own-child
## 1382 Never-married Sales Not-in-family
## 1383 Never-married Prof-specialty Not-in-family
## 1384 Never-married Handlers-cleaners Own-child
## 1385 Married-civ-spouse Exec-managerial Husband
## 1386 Never-married Machine-op-inspct Own-child
## 1387 Never-married Sales Not-in-family
## 1388 Divorced Exec-managerial Other-relative
## 1389 Never-married ? Own-child
## 1390 Never-married Other-service Own-child
## 1391 Never-married Tech-support Not-in-family
## 1392 Married-civ-spouse Craft-repair Husband
## 1393 Divorced Sales Unmarried
## 1394 Never-married Adm-clerical Unmarried
## 1395 Never-married Machine-op-inspct Not-in-family
## 1396 Never-married Transport-moving Unmarried
## 1397 Married-civ-spouse Transport-moving Husband
## 1398 Married-civ-spouse Sales Husband
## 1399 Married-civ-spouse Handlers-cleaners Husband
## 1400 Married-civ-spouse Transport-moving Husband
## 1401 Never-married Adm-clerical Not-in-family
## 1402 Married-civ-spouse Tech-support Husband
## 1403 Divorced Adm-clerical Not-in-family
## 1404 Married-civ-spouse Craft-repair Husband
## 1405 Divorced ? Unmarried
## 1406 Married-civ-spouse Other-service Husband
## 1407 Divorced Sales Own-child
## 1408 Married-civ-spouse Craft-repair Wife
## 1409 Divorced Transport-moving Not-in-family
## 1410 Married-civ-spouse Transport-moving Husband
## 1411 Widowed Other-service Unmarried
## 1412 Married-civ-spouse Other-service Husband
## 1413 Married-civ-spouse Prof-specialty Husband
## 1414 Married-civ-spouse Exec-managerial Husband
## 1415 Married-civ-spouse Exec-managerial Husband
## 1416 Married-civ-spouse Adm-clerical Husband
## 1417 Never-married Other-service Not-in-family
## 1418 Married-civ-spouse Sales Husband
## 1419 Married-civ-spouse Prof-specialty Wife
## 1420 Never-married Adm-clerical Not-in-family
## 1421 Never-married ? Own-child
## 1422 Married-civ-spouse Handlers-cleaners Husband
## 1423 Never-married Adm-clerical Own-child
## 1424 Divorced Other-service Not-in-family
## 1425 Married-civ-spouse Adm-clerical Husband
## 1426 Divorced Machine-op-inspct Unmarried
## 1427 Married-civ-spouse Farming-fishing Husband
## 1428 Married-civ-spouse Other-service Wife
## 1429 Never-married Machine-op-inspct Not-in-family
## 1430 Married-civ-spouse Transport-moving Husband
## 1431 Married-civ-spouse Machine-op-inspct Wife
## 1432 Divorced Exec-managerial Not-in-family
## 1433 Divorced Sales Not-in-family
## 1434 Never-married ? Own-child
## 1435 Never-married Protective-serv Not-in-family
## 1436 Married-civ-spouse Transport-moving Wife
## 1437 Never-married Other-service Not-in-family
## 1438 Married-civ-spouse Adm-clerical Wife
## 1439 Divorced Prof-specialty Unmarried
## 1440 Married-civ-spouse Sales Husband
## 1441 Married-civ-spouse Craft-repair Husband
## 1442 Married-civ-spouse ? Husband
## 1443 Married-civ-spouse Exec-managerial Husband
## 1444 Divorced Craft-repair Not-in-family
## 1445 Married-civ-spouse Other-service Husband
## 1446 Married-civ-spouse Transport-moving Husband
## 1447 Married-civ-spouse Sales Husband
## 1448 Married-civ-spouse Exec-managerial Husband
## 1449 Married-civ-spouse Exec-managerial Husband
## 1450 Married-civ-spouse Exec-managerial Husband
## 1451 Divorced Craft-repair Not-in-family
## 1452 Widowed Transport-moving Unmarried
## 1453 Never-married ? Own-child
## 1454 Married-civ-spouse Exec-managerial Husband
## 1455 Married-civ-spouse Craft-repair Husband
## 1456 Widowed Adm-clerical Unmarried
## 1457 Divorced Craft-repair Not-in-family
## 1458 Never-married Transport-moving Not-in-family
## 1459 Widowed ? Not-in-family
## 1460 Married-civ-spouse Prof-specialty Husband
## 1461 Married-civ-spouse Exec-managerial Husband
## 1462 Never-married Machine-op-inspct Not-in-family
## 1463 Never-married Craft-repair Not-in-family
## 1464 Married-civ-spouse Transport-moving Husband
## 1465 Divorced Craft-repair Not-in-family
## 1466 Married-civ-spouse Adm-clerical Husband
## 1467 Married-civ-spouse Craft-repair Husband
## 1468 Divorced Exec-managerial Not-in-family
## 1469 Never-married Protective-serv Not-in-family
## 1470 Married-civ-spouse Prof-specialty Husband
## 1471 Married-civ-spouse Transport-moving Husband
## 1472 Never-married Prof-specialty Own-child
## 1473 Never-married Prof-specialty Not-in-family
## 1474 Married-civ-spouse Prof-specialty Husband
## 1475 Never-married Handlers-cleaners Own-child
## 1476 Married-civ-spouse Adm-clerical Husband
## 1477 Divorced Other-service Unmarried
## 1478 Never-married Adm-clerical Own-child
## 1479 Never-married Adm-clerical Own-child
## 1480 Never-married Other-service Not-in-family
## 1481 Married-spouse-absent Craft-repair Not-in-family
## 1482 Married-civ-spouse Craft-repair Husband
## 1483 Married-civ-spouse Sales Husband
## 1484 Never-married Craft-repair Not-in-family
## 1485 Married-civ-spouse Prof-specialty Husband
## 1486 Married-civ-spouse Farming-fishing Husband
## 1487 Never-married Adm-clerical Unmarried
## 1488 Never-married Other-service Own-child
## 1489 Divorced Adm-clerical Unmarried
## 1490 Divorced Exec-managerial Unmarried
## 1491 Married-civ-spouse Sales Husband
## 1492 Divorced Exec-managerial Not-in-family
## 1493 Married-civ-spouse Adm-clerical Husband
## 1494 Married-civ-spouse Adm-clerical Husband
## 1495 Never-married Handlers-cleaners Other-relative
## 1496 Married-civ-spouse Craft-repair Husband
## 1497 Divorced Sales Not-in-family
## 1498 Divorced Sales Not-in-family
## 1499 Married-civ-spouse Exec-managerial Husband
## 1500 Never-married Adm-clerical Not-in-family
## 1501 Married-civ-spouse Exec-managerial Husband
## 1502 Divorced Machine-op-inspct Not-in-family
## 1503 Never-married Other-service Not-in-family
## 1504 Married-civ-spouse Exec-managerial Husband
## 1505 Married-civ-spouse ? Husband
## 1506 Married-civ-spouse Craft-repair Husband
## 1507 Married-civ-spouse Exec-managerial Husband
## 1508 Married-civ-spouse Sales Husband
## 1509 Never-married Adm-clerical Not-in-family
## 1510 Married-civ-spouse Adm-clerical Husband
## 1511 Married-spouse-absent Prof-specialty Unmarried
## 1512 Married-civ-spouse Prof-specialty Husband
## 1513 Married-civ-spouse Prof-specialty Husband
## 1514 Married-civ-spouse Prof-specialty Husband
## 1515 Married-civ-spouse Exec-managerial Husband
## 1516 Divorced Other-service Not-in-family
## 1517 Married-civ-spouse Adm-clerical Husband
## 1518 Married-civ-spouse Adm-clerical Wife
## 1519 Never-married Exec-managerial Own-child
## 1520 Never-married Other-service Own-child
## 1521 Married-civ-spouse Protective-serv Husband
## 1522 Divorced Other-service Not-in-family
## 1523 Divorced Exec-managerial Unmarried
## 1524 Never-married Farming-fishing Own-child
## 1525 Never-married Prof-specialty Not-in-family
## 1526 Never-married Handlers-cleaners Not-in-family
## 1527 Divorced Other-service Unmarried
## 1528 Married-civ-spouse Machine-op-inspct Husband
## 1529 Married-civ-spouse Exec-managerial Husband
## 1530 Divorced Exec-managerial Unmarried
## 1531 Married-civ-spouse Craft-repair Husband
## 1532 Married-civ-spouse Machine-op-inspct Husband
## 1533 Divorced Exec-managerial Unmarried
## 1534 Married-civ-spouse Transport-moving Husband
## 1535 Divorced Other-service Not-in-family
## 1536 Never-married Prof-specialty Not-in-family
## 1537 Married-civ-spouse Exec-managerial Husband
## 1538 Married-civ-spouse Machine-op-inspct Husband
## 1539 Married-civ-spouse Machine-op-inspct Wife
## 1540 Separated Other-service Not-in-family
## 1541 Divorced Adm-clerical Own-child
## 1542 Married-civ-spouse Prof-specialty Husband
## 1543 Separated Protective-serv Not-in-family
## 1544 Married-civ-spouse Craft-repair Husband
## 1545 Divorced ? Not-in-family
## 1546 Married-civ-spouse Craft-repair Husband
## 1547 Never-married Sales Own-child
## 1548 Married-civ-spouse Adm-clerical Husband
## 1549 Married-civ-spouse Craft-repair Husband
## 1550 Never-married Machine-op-inspct Unmarried
## 1551 Never-married Adm-clerical Own-child
## 1552 Never-married Handlers-cleaners Own-child
## 1553 Divorced Machine-op-inspct Not-in-family
## 1554 Never-married Adm-clerical Not-in-family
## 1555 Married-civ-spouse Prof-specialty Husband
## 1556 Divorced Transport-moving Unmarried
## 1557 Never-married Adm-clerical Not-in-family
## 1558 Never-married Sales Not-in-family
## 1559 Never-married Transport-moving Not-in-family
## 1560 Never-married Other-service Other-relative
## 1561 Never-married Handlers-cleaners Own-child
## 1562 Married-civ-spouse Craft-repair Husband
## 1563 Widowed ? Not-in-family
## 1564 Married-civ-spouse Protective-serv Husband
## 1565 Separated Other-service Other-relative
## 1566 Never-married Other-service Not-in-family
## 1567 Married-civ-spouse Farming-fishing Husband
## 1568 Never-married Other-service Not-in-family
## 1569 Married-civ-spouse Craft-repair Husband
## 1570 Never-married ? Own-child
## 1571 Never-married ? Own-child
## 1572 Married-civ-spouse Craft-repair Husband
## 1573 Never-married Other-service Own-child
## 1574 Never-married Handlers-cleaners Own-child
## 1575 Separated ? Own-child
## 1576 Married-civ-spouse Adm-clerical Husband
## 1577 Never-married Farming-fishing Not-in-family
## 1578 Never-married Tech-support Not-in-family
## 1579 Never-married Prof-specialty Not-in-family
## 1580 Widowed ? Not-in-family
## 1581 Married-civ-spouse Sales Husband
## 1582 Never-married Sales Own-child
## 1583 Married-civ-spouse Sales Husband
## 1584 Divorced Adm-clerical Not-in-family
## 1585 Married-civ-spouse Farming-fishing Husband
## 1586 Married-civ-spouse Craft-repair Husband
## 1587 Divorced Exec-managerial Unmarried
## 1588 Divorced Craft-repair Own-child
## 1589 Never-married Exec-managerial Own-child
## 1590 Never-married Adm-clerical Not-in-family
## 1591 Married-civ-spouse Protective-serv Husband
## 1592 Married-civ-spouse Prof-specialty Husband
## 1593 Never-married ? Own-child
## 1594 Married-civ-spouse Prof-specialty Husband
## 1595 Married-civ-spouse Other-service Husband
## 1596 Never-married Prof-specialty Not-in-family
## 1597 Married-civ-spouse Adm-clerical Wife
## 1598 Never-married Sales Not-in-family
## 1599 Never-married Machine-op-inspct Own-child
## 1600 Never-married Sales Not-in-family
## 1601 Married-civ-spouse Craft-repair Husband
## 1602 Divorced Exec-managerial Not-in-family
## 1603 Never-married Prof-specialty Own-child
## 1604 Married-civ-spouse Machine-op-inspct Husband
## 1605 Married-civ-spouse Tech-support Husband
## 1606 Married-civ-spouse Adm-clerical Husband
## 1607 Never-married ? Unmarried
## 1608 Separated Craft-repair Own-child
## 1609 Married-civ-spouse Craft-repair Husband
## 1610 Divorced Other-service Unmarried
## 1611 Divorced Sales Not-in-family
## 1612 Married-civ-spouse Prof-specialty Wife
## 1613 Married-civ-spouse Prof-specialty Husband
## 1614 Married-civ-spouse Machine-op-inspct Husband
## 1615 Never-married Adm-clerical Own-child
## 1616 Married-civ-spouse Prof-specialty Husband
## 1617 Married-civ-spouse Prof-specialty Husband
## 1618 Married-civ-spouse Exec-managerial Wife
## 1619 Never-married Craft-repair Other-relative
## 1620 Married-civ-spouse Transport-moving Husband
## 1621 Married-civ-spouse Sales Husband
## 1622 Married-civ-spouse Exec-managerial Husband
## 1623 Never-married Other-service Own-child
## 1624 Married-civ-spouse Exec-managerial Husband
## 1625 Never-married Other-service Not-in-family
## 1626 Married-civ-spouse Prof-specialty Husband
## 1627 Married-civ-spouse Craft-repair Husband
## 1628 Married-civ-spouse Sales Wife
## 1629 Married-civ-spouse Machine-op-inspct Wife
## 1630 Married-civ-spouse ? Wife
## 1631 Married-civ-spouse Other-service Husband
## 1632 Divorced Other-service Not-in-family
## 1633 Married-civ-spouse Adm-clerical Husband
## 1634 Never-married Farming-fishing Not-in-family
## 1635 Married-civ-spouse Machine-op-inspct Husband
## 1636 Married-civ-spouse Exec-managerial Husband
## 1637 Divorced Other-service Not-in-family
## 1638 Never-married Sales Not-in-family
## 1639 Married-civ-spouse Prof-specialty Husband
## 1640 Married-civ-spouse Machine-op-inspct Husband
## 1641 Divorced Other-service Other-relative
## 1642 Married-civ-spouse Prof-specialty Husband
## 1643 Married-civ-spouse Prof-specialty Husband
## 1644 Married-civ-spouse Prof-specialty Husband
## 1645 Married-civ-spouse Prof-specialty Wife
## 1646 Married-civ-spouse Adm-clerical Wife
## 1647 Married-civ-spouse Machine-op-inspct Husband
## 1648 Married-civ-spouse Sales Husband
## 1649 Married-civ-spouse Exec-managerial Husband
## 1650 Divorced Adm-clerical Not-in-family
## 1651 Never-married Handlers-cleaners Own-child
## 1652 Married-civ-spouse Craft-repair Husband
## 1653 Never-married Sales Own-child
## 1654 Married-civ-spouse Protective-serv Husband
## 1655 Married-civ-spouse Craft-repair Husband
## 1656 Married-civ-spouse Prof-specialty Husband
## 1657 Separated ? Unmarried
## 1658 Married-civ-spouse Craft-repair Husband
## 1659 Never-married Sales Own-child
## 1660 Married-civ-spouse Craft-repair Husband
## 1661 Married-civ-spouse Craft-repair Husband
## 1662 Married-civ-spouse Exec-managerial Husband
## 1663 Never-married Prof-specialty Not-in-family
## 1664 Married-civ-spouse Protective-serv Husband
## 1665 Married-civ-spouse Handlers-cleaners Husband
## 1666 Divorced ? Unmarried
## 1667 Married-civ-spouse Sales Husband
## 1668 Never-married Sales Not-in-family
## 1669 Married-civ-spouse Adm-clerical Husband
## 1670 Married-civ-spouse Prof-specialty Husband
## 1671 Married-civ-spouse Sales Husband
## 1672 Never-married Exec-managerial Unmarried
## 1673 Married-civ-spouse Craft-repair Husband
## 1674 Never-married Sales Own-child
## 1675 Married-civ-spouse Adm-clerical Wife
## 1676 Divorced Exec-managerial Unmarried
## 1677 Married-civ-spouse ? Husband
## 1678 Never-married Craft-repair Not-in-family
## 1679 Married-civ-spouse Craft-repair Husband
## 1680 Married-civ-spouse Transport-moving Husband
## 1681 Divorced Other-service Unmarried
## 1682 Married-civ-spouse Machine-op-inspct Husband
## 1683 Married-civ-spouse Exec-managerial Husband
## 1684 Married-civ-spouse Machine-op-inspct Wife
## 1685 Never-married Transport-moving Own-child
## 1686 Married-civ-spouse Exec-managerial Husband
## 1687 Never-married Other-service Not-in-family
## 1688 Married-civ-spouse Exec-managerial Husband
## 1689 Divorced Other-service Not-in-family
## 1690 Married-civ-spouse Prof-specialty Husband
## 1691 Never-married Other-service Not-in-family
## 1692 Never-married Adm-clerical Own-child
## 1693 Never-married Sales Own-child
## 1694 Married-civ-spouse Exec-managerial Wife
## 1695 Married-civ-spouse Sales Husband
## 1696 Married-civ-spouse Machine-op-inspct Husband
## 1697 Married-civ-spouse Exec-managerial Wife
## 1698 Married-civ-spouse Handlers-cleaners Husband
## 1699 Divorced Craft-repair Not-in-family
## 1700 Married-civ-spouse Prof-specialty Husband
## 1701 Divorced Adm-clerical Unmarried
## 1702 Never-married Other-service Own-child
## 1703 Married-civ-spouse Handlers-cleaners Husband
## 1704 Divorced Craft-repair Not-in-family
## 1705 Divorced ? Not-in-family
## 1706 Never-married Machine-op-inspct Not-in-family
## 1707 Married-civ-spouse Craft-repair Husband
## 1708 Widowed ? Not-in-family
## 1709 Never-married Machine-op-inspct Not-in-family
## 1710 Married-civ-spouse Exec-managerial Husband
## 1711 Widowed Sales Other-relative
## 1712 Married-civ-spouse Craft-repair Husband
## 1713 Married-civ-spouse Machine-op-inspct Husband
## 1714 Married-civ-spouse Sales Husband
## 1715 Married-civ-spouse Machine-op-inspct Husband
## 1716 Divorced Adm-clerical Unmarried
## 1717 Divorced Adm-clerical Not-in-family
## 1718 Married-civ-spouse Prof-specialty Husband
## 1719 Married-civ-spouse Sales Husband
## 1720 Married-civ-spouse Transport-moving Husband
## 1721 Divorced Adm-clerical Not-in-family
## 1722 Married-civ-spouse Protective-serv Husband
## 1723 Divorced Sales Not-in-family
## 1724 Married-civ-spouse Exec-managerial Husband
## 1725 Never-married Other-service Not-in-family
## 1726 Divorced Craft-repair Not-in-family
## 1727 Married-civ-spouse Craft-repair Husband
## 1728 Divorced Sales Not-in-family
## 1729 Married-civ-spouse Prof-specialty Wife
## 1730 Married-civ-spouse Other-service Husband
## 1731 Never-married Exec-managerial Own-child
## 1732 Never-married Exec-managerial Not-in-family
## 1733 Never-married Sales Not-in-family
## 1734 Never-married Other-service Not-in-family
## 1735 Never-married Sales Not-in-family
## 1736 Married-civ-spouse Exec-managerial Husband
## 1737 Never-married Craft-repair Other-relative
## 1738 Never-married Other-service Not-in-family
## 1739 Divorced Other-service Not-in-family
## 1740 Married-civ-spouse Transport-moving Husband
## 1741 Married-civ-spouse Adm-clerical Husband
## 1742 Married-civ-spouse Other-service Wife
## 1743 Divorced Craft-repair Not-in-family
## 1744 Never-married Adm-clerical Own-child
## 1745 Married-civ-spouse Craft-repair Husband
## 1746 Widowed Exec-managerial Not-in-family
## 1747 Never-married Prof-specialty Own-child
## 1748 Never-married Adm-clerical Not-in-family
## 1749 Never-married Other-service Not-in-family
## 1750 Widowed Adm-clerical Unmarried
## 1751 Never-married Machine-op-inspct Not-in-family
## 1752 Divorced Transport-moving Not-in-family
## 1753 Divorced Adm-clerical Unmarried
## 1754 Divorced Machine-op-inspct Not-in-family
## 1755 Married-civ-spouse Exec-managerial Husband
## 1756 Married-civ-spouse Exec-managerial Own-child
## 1757 Divorced Exec-managerial Not-in-family
## 1758 Never-married Farming-fishing Not-in-family
## 1759 Married-civ-spouse ? Husband
## 1760 Never-married Other-service Own-child
## 1761 Married-civ-spouse Prof-specialty Wife
## 1762 Married-civ-spouse ? Husband
## 1763 Divorced Adm-clerical Unmarried
## 1764 Never-married Machine-op-inspct Own-child
## 1765 Divorced Exec-managerial Not-in-family
## 1766 Married-civ-spouse Prof-specialty Wife
## 1767 Never-married Handlers-cleaners Other-relative
## 1768 Married-civ-spouse Machine-op-inspct Husband
## 1769 Married-civ-spouse Protective-serv Husband
## 1770 Married-civ-spouse Tech-support Husband
## 1771 Married-spouse-absent Protective-serv Not-in-family
## 1772 Married-civ-spouse Exec-managerial Husband
## 1773 Married-civ-spouse Craft-repair Husband
## 1774 Never-married ? Own-child
## 1775 Married-civ-spouse Other-service Wife
## 1776 Married-civ-spouse Machine-op-inspct Husband
## 1777 Married-civ-spouse Machine-op-inspct Husband
## 1778 Widowed Farming-fishing Unmarried
## 1779 Never-married ? Own-child
## 1780 Married-civ-spouse Sales Husband
## 1781 Married-civ-spouse Sales Husband
## 1782 Married-civ-spouse Exec-managerial Husband
## 1783 Never-married Other-service Own-child
## 1784 Married-civ-spouse Exec-managerial Husband
## 1785 Never-married Adm-clerical Not-in-family
## 1786 Married-spouse-absent Prof-specialty Not-in-family
## 1787 Married-civ-spouse Prof-specialty Wife
## 1788 Married-civ-spouse Exec-managerial Husband
## 1789 Divorced Adm-clerical Unmarried
## 1790 Married-spouse-absent Handlers-cleaners Unmarried
## 1791 Married-civ-spouse Tech-support Husband
## 1792 Divorced Craft-repair Not-in-family
## 1793 Never-married Other-service Unmarried
## 1794 Never-married Adm-clerical Not-in-family
## 1795 Married-civ-spouse Machine-op-inspct Husband
## 1796 Married-civ-spouse Sales Husband
## 1797 Never-married Adm-clerical Not-in-family
## 1798 Married-civ-spouse Handlers-cleaners Husband
## 1799 Married-civ-spouse Adm-clerical Husband
## 1800 Divorced Sales Not-in-family
## 1801 Married-civ-spouse Adm-clerical Husband
## 1802 Widowed Adm-clerical Unmarried
## 1803 Divorced Other-service Not-in-family
## 1804 Married-civ-spouse Prof-specialty Husband
## 1805 Never-married Exec-managerial Not-in-family
## 1806 Never-married Prof-specialty Not-in-family
## 1807 Married-civ-spouse Machine-op-inspct Husband
## 1808 Married-civ-spouse Sales Husband
## 1809 Married-civ-spouse Protective-serv Husband
## 1810 Divorced Exec-managerial Unmarried
## 1811 Married-civ-spouse Exec-managerial Husband
## 1812 Married-civ-spouse Exec-managerial Husband
## 1813 Married-civ-spouse Transport-moving Husband
## 1814 Widowed Sales Own-child
## 1815 Married-civ-spouse Sales Husband
## 1816 Never-married Handlers-cleaners Own-child
## 1817 Married-civ-spouse Craft-repair Husband
## 1818 Divorced Craft-repair Unmarried
## 1819 Married-civ-spouse Craft-repair Husband
## 1820 Married-civ-spouse Adm-clerical Husband
## 1821 Married-civ-spouse Handlers-cleaners Husband
## 1822 Never-married Craft-repair Not-in-family
## 1823 Never-married Sales Own-child
## 1824 Never-married ? Own-child
## 1825 Married-civ-spouse Transport-moving Husband
## 1826 Divorced Exec-managerial Unmarried
## 1827 Never-married Prof-specialty Unmarried
## 1828 Divorced Adm-clerical Unmarried
## 1829 Never-married Sales Own-child
## 1830 Divorced Transport-moving Unmarried
## 1831 Never-married Other-service Own-child
## 1832 Widowed Sales Not-in-family
## 1833 Married-civ-spouse Prof-specialty Husband
## 1834 Divorced Prof-specialty Not-in-family
## 1835 Never-married Craft-repair Not-in-family
## 1836 Never-married Adm-clerical Not-in-family
## 1837 Married-civ-spouse Sales Husband
## 1838 Married-civ-spouse Farming-fishing Husband
## 1839 Divorced Adm-clerical Unmarried
## 1840 Married-civ-spouse Adm-clerical Husband
## 1841 Never-married Craft-repair Not-in-family
## 1842 Married-civ-spouse Transport-moving Husband
## 1843 Married-civ-spouse Craft-repair Wife
## 1844 Married-civ-spouse Craft-repair Husband
## 1845 Never-married Transport-moving Not-in-family
## 1846 Never-married Transport-moving Not-in-family
## 1847 Never-married ? Own-child
## 1848 Never-married Other-service Own-child
## 1849 Never-married Prof-specialty Not-in-family
## 1850 Married-civ-spouse Craft-repair Husband
## 1851 Married-civ-spouse Handlers-cleaners Husband
## 1852 Married-civ-spouse ? Husband
## 1853 Never-married Handlers-cleaners Own-child
## 1854 Divorced Craft-repair Unmarried
## 1855 Never-married Transport-moving Unmarried
## 1856 Never-married Prof-specialty Own-child
## 1857 Never-married Prof-specialty Not-in-family
## 1858 Never-married Other-service Own-child
## 1859 Never-married Other-service Own-child
## 1860 Married-civ-spouse Adm-clerical Wife
## 1861 Divorced Sales Not-in-family
## 1862 Married-civ-spouse Machine-op-inspct Husband
## 1863 Married-civ-spouse Exec-managerial Husband
## 1864 Never-married Craft-repair Other-relative
## 1865 Married-civ-spouse Protective-serv Husband
## 1866 Married-spouse-absent ? Unmarried
## 1867 Never-married Other-service Not-in-family
## 1868 Divorced Adm-clerical Not-in-family
## 1869 Divorced Other-service Not-in-family
## 1870 Married-civ-spouse Prof-specialty Husband
## 1871 Married-civ-spouse Transport-moving Husband
## 1872 Never-married Adm-clerical Own-child
## 1873 Married-civ-spouse Exec-managerial Husband
## 1874 Never-married Adm-clerical Not-in-family
## 1875 Never-married Other-service Own-child
## 1876 Married-civ-spouse Sales Husband
## 1877 Separated Exec-managerial Not-in-family
## 1878 Married-civ-spouse Sales Husband
## 1879 Never-married ? Own-child
## 1880 Divorced Adm-clerical Not-in-family
## 1881 Never-married Other-service Unmarried
## 1882 Married-civ-spouse Tech-support Husband
## 1883 Married-civ-spouse Prof-specialty Wife
## 1884 Never-married Exec-managerial Not-in-family
## 1885 Never-married Exec-managerial Unmarried
## 1886 Never-married Other-service Not-in-family
## 1887 Never-married Sales Not-in-family
## 1888 Married-civ-spouse Farming-fishing Husband
## 1889 Never-married Other-service Own-child
## 1890 Married-civ-spouse Exec-managerial Wife
## 1891 Married-civ-spouse Farming-fishing Husband
## 1892 Widowed Exec-managerial Not-in-family
## 1893 Divorced Other-service Other-relative
## 1894 Married-civ-spouse Prof-specialty Husband
## 1895 Widowed Sales Not-in-family
## 1896 Separated Exec-managerial Not-in-family
## 1897 Married-civ-spouse Sales Husband
## 1898 Never-married Transport-moving Not-in-family
## 1899 Married-civ-spouse Machine-op-inspct Husband
## 1900 Never-married Prof-specialty Own-child
## 1901 Never-married Other-service Not-in-family
## 1902 Widowed Farming-fishing Unmarried
## 1903 Never-married Transport-moving Not-in-family
## 1904 Never-married Adm-clerical Not-in-family
## 1905 Married-civ-spouse Craft-repair Husband
## 1906 Married-civ-spouse Machine-op-inspct Husband
## 1907 Divorced Prof-specialty Unmarried
## 1908 Never-married Prof-specialty Not-in-family
## 1909 Married-civ-spouse Transport-moving Husband
## 1910 Married-civ-spouse Transport-moving Husband
## 1911 Married-civ-spouse Craft-repair Husband
## 1912 Never-married Machine-op-inspct Unmarried
## 1913 Married-civ-spouse Exec-managerial Husband
## 1914 Married-civ-spouse Craft-repair Husband
## 1915 Divorced Craft-repair Not-in-family
## 1916 Married-civ-spouse Exec-managerial Husband
## 1917 Married-civ-spouse Sales Husband
## 1918 Married-civ-spouse Protective-serv Husband
## 1919 Never-married Prof-specialty Own-child
## 1920 Never-married Adm-clerical Not-in-family
## 1921 Separated Adm-clerical Unmarried
## 1922 Married-civ-spouse Craft-repair Husband
## 1923 Married-civ-spouse Tech-support Husband
## 1924 Never-married ? Own-child
## 1925 Never-married Sales Own-child
## 1926 Never-married Exec-managerial Not-in-family
## 1927 Never-married Sales Not-in-family
## 1928 Married-civ-spouse Handlers-cleaners Husband
## 1929 Never-married Craft-repair Own-child
## 1930 Never-married Prof-specialty Not-in-family
## 1931 Divorced Adm-clerical Not-in-family
## 1932 Married-civ-spouse ? Own-child
## 1933 Married-civ-spouse Machine-op-inspct Other-relative
## 1934 Married-civ-spouse Tech-support Other-relative
## 1935 Married-civ-spouse Sales Husband
## 1936 Married-civ-spouse Exec-managerial Husband
## 1937 Separated Prof-specialty Unmarried
## 1938 Married-spouse-absent Exec-managerial Unmarried
## 1939 Married-civ-spouse Exec-managerial Husband
## 1940 Never-married Adm-clerical Not-in-family
## 1941 Married-civ-spouse Farming-fishing Husband
## 1942 Married-civ-spouse Sales Husband
## 1943 Divorced Protective-serv Unmarried
## 1944 Married-civ-spouse Adm-clerical Wife
## 1945 Never-married Adm-clerical Other-relative
## 1946 Married-civ-spouse Exec-managerial Husband
## 1947 Divorced Prof-specialty Not-in-family
## 1948 Never-married Adm-clerical Unmarried
## 1949 Married-civ-spouse Prof-specialty Husband
## 1950 Married-civ-spouse Exec-managerial Husband
## 1951 Married-civ-spouse Exec-managerial Husband
## 1952 Married-civ-spouse Exec-managerial Husband
## 1953 Never-married Prof-specialty Not-in-family
## 1954 Married-civ-spouse Machine-op-inspct Husband
## 1955 Married-civ-spouse Protective-serv Husband
## 1956 Never-married Transport-moving Own-child
## 1957 Married-civ-spouse Sales Husband
## 1958 Widowed Other-service Not-in-family
## 1959 Never-married Prof-specialty Not-in-family
## 1960 Married-civ-spouse Exec-managerial Husband
## 1961 Never-married Adm-clerical Own-child
## 1962 Married-civ-spouse Prof-specialty Husband
## 1963 Married-civ-spouse Prof-specialty Husband
## 1964 Married-civ-spouse Sales Husband
## 1965 Never-married Transport-moving Unmarried
## 1966 Divorced Other-service Unmarried
## 1967 Never-married Sales Own-child
## 1968 Never-married Adm-clerical Unmarried
## 1969 Married-civ-spouse Craft-repair Husband
## 1970 Married-civ-spouse Sales Husband
## 1971 Never-married Sales Not-in-family
## 1972 Separated ? Not-in-family
## 1973 Never-married Prof-specialty Not-in-family
## 1974 Married-civ-spouse Other-service Husband
## 1975 Married-civ-spouse Sales Husband
## 1976 Married-spouse-absent Prof-specialty Not-in-family
## 1977 Never-married Exec-managerial Not-in-family
## 1978 Never-married Sales Not-in-family
## 1979 Married-civ-spouse Farming-fishing Husband
## 1980 Divorced Handlers-cleaners Own-child
## 1981 Divorced Prof-specialty Not-in-family
## 1982 Married-civ-spouse Adm-clerical Husband
## 1983 Never-married Exec-managerial Not-in-family
## 1984 Married-civ-spouse Craft-repair Husband
## 1985 Married-civ-spouse Exec-managerial Husband
## 1986 Never-married Machine-op-inspct Not-in-family
## 1987 Married-civ-spouse Sales Husband
## 1988 Married-AF-spouse ? Wife
## 1989 Married-civ-spouse Craft-repair Husband
## 1990 Never-married Sales Not-in-family
## 1991 Never-married Priv-house-serv Not-in-family
## 1992 Never-married Handlers-cleaners Not-in-family
## 1993 Never-married Sales Not-in-family
## 1994 Divorced Exec-managerial Unmarried
## 1995 Never-married Exec-managerial Unmarried
## 1996 Divorced Machine-op-inspct Not-in-family
## 1997 Married-civ-spouse Sales Husband
## 1998 Married-spouse-absent Prof-specialty Not-in-family
## 1999 Married-civ-spouse Sales Husband
## 2000 Divorced Craft-repair Unmarried
## 2001 Never-married Craft-repair Unmarried
## 2002 Never-married Sales Other-relative
## 2003 Married-civ-spouse Farming-fishing Husband
## 2004 Married-civ-spouse Exec-managerial Wife
## 2005 Separated Sales Not-in-family
## 2006 Never-married Farming-fishing Own-child
## 2007 Never-married Adm-clerical Other-relative
## 2008 Married-civ-spouse Craft-repair Husband
## 2009 Never-married Craft-repair Own-child
## 2010 Married-spouse-absent Other-service Not-in-family
## 2011 Never-married Exec-managerial Not-in-family
## 2012 Never-married Other-service Own-child
## 2013 Never-married Other-service Not-in-family
## 2014 Never-married Prof-specialty Not-in-family
## 2015 Married-civ-spouse Prof-specialty Husband
## 2016 Never-married Prof-specialty Not-in-family
## 2017 Never-married Prof-specialty Not-in-family
## 2018 Never-married Adm-clerical Not-in-family
## 2019 Married-civ-spouse Exec-managerial Husband
## 2020 Never-married Craft-repair Own-child
## 2021 Married-civ-spouse Other-service Wife
## 2022 Never-married Prof-specialty Not-in-family
## 2023 Divorced Other-service Other-relative
## 2024 Divorced Other-service Own-child
## 2025 Married-civ-spouse Exec-managerial Husband
## 2026 Never-married ? Not-in-family
## 2027 Married-civ-spouse Machine-op-inspct Husband
## 2028 Never-married Handlers-cleaners Not-in-family
## 2029 Married-civ-spouse Tech-support Husband
## 2030 Married-civ-spouse Prof-specialty Husband
## 2031 Married-civ-spouse Exec-managerial Wife
## 2032 Married-civ-spouse Prof-specialty Wife
## 2033 Married-civ-spouse Craft-repair Husband
## 2034 Married-civ-spouse Craft-repair Husband
## 2035 Married-civ-spouse Sales Own-child
## 2036 Separated Exec-managerial Unmarried
## 2037 Never-married ? Other-relative
## 2038 Married-civ-spouse Craft-repair Husband
## 2039 Married-civ-spouse Prof-specialty Husband
## 2040 Married-civ-spouse Exec-managerial Husband
## 2041 Never-married Exec-managerial Unmarried
## 2042 Widowed Other-service Unmarried
## 2043 Married-civ-spouse Farming-fishing Husband
## 2044 Married-civ-spouse Exec-managerial Husband
## 2045 Divorced Exec-managerial Not-in-family
## 2046 Never-married Adm-clerical Own-child
## 2047 Never-married ? Not-in-family
## 2048 Never-married Craft-repair Not-in-family
## 2049 Married-civ-spouse Craft-repair Husband
## 2050 Married-spouse-absent Prof-specialty Own-child
## 2051 Never-married Adm-clerical Not-in-family
## 2052 Married-civ-spouse Sales Husband
## 2053 Never-married Other-service Other-relative
## 2054 Never-married Sales Unmarried
## 2055 Never-married Sales Not-in-family
## 2056 Married-civ-spouse Transport-moving Husband
## 2057 Never-married Prof-specialty Not-in-family
## 2058 Married-civ-spouse Transport-moving Husband
## 2059 Never-married Handlers-cleaners Own-child
## 2060 Never-married Adm-clerical Own-child
## 2061 Divorced Other-service Not-in-family
## 2062 Never-married ? Own-child
## 2063 Never-married Prof-specialty Own-child
## 2064 Married-civ-spouse Exec-managerial Husband
## 2065 Divorced Adm-clerical Not-in-family
## 2066 Never-married Priv-house-serv Unmarried
## 2067 Married-civ-spouse Exec-managerial Husband
## 2068 Widowed Adm-clerical Not-in-family
## 2069 Married-civ-spouse Other-service Wife
## 2070 Divorced Exec-managerial Own-child
## 2071 Never-married Prof-specialty Not-in-family
## 2072 Married-civ-spouse Farming-fishing Husband
## 2073 Married-civ-spouse ? Husband
## 2074 Married-civ-spouse Exec-managerial Husband
## 2075 Married-civ-spouse Protective-serv Husband
## 2076 Never-married Adm-clerical Own-child
## 2077 Divorced Other-service Unmarried
## 2078 Never-married Machine-op-inspct Own-child
## 2079 Never-married Farming-fishing Own-child
## 2080 Married-civ-spouse Exec-managerial Husband
## 2081 Separated Protective-serv Unmarried
## 2082 Married-civ-spouse Exec-managerial Husband
## 2083 Separated Exec-managerial Unmarried
## 2084 Divorced Machine-op-inspct Not-in-family
## 2085 Married-civ-spouse ? Wife
## 2086 Married-civ-spouse Exec-managerial Husband
## 2087 Married-civ-spouse Sales Husband
## 2088 Never-married Adm-clerical Own-child
## 2089 Married-civ-spouse Adm-clerical Wife
## 2090 Married-civ-spouse Other-service Husband
## 2091 Married-civ-spouse Sales Husband
## 2092 Never-married ? Not-in-family
## 2093 Divorced Sales Not-in-family
## 2094 Married-civ-spouse Sales Husband
## 2095 Never-married ? Own-child
## 2096 Married-civ-spouse Craft-repair Husband
## 2097 Never-married Prof-specialty Not-in-family
## 2098 Divorced Adm-clerical Not-in-family
## 2099 Married-civ-spouse Prof-specialty Husband
## 2100 Married-civ-spouse Craft-repair Husband
## 2101 Divorced Exec-managerial Not-in-family
## 2102 Married-civ-spouse Craft-repair Husband
## 2103 Divorced Craft-repair Unmarried
## 2104 Married-civ-spouse Sales Husband
## 2105 Married-civ-spouse Transport-moving Husband
## 2106 Never-married ? Own-child
## 2107 Married-civ-spouse Craft-repair Husband
## 2108 Married-spouse-absent Craft-repair Other-relative
## 2109 Married-civ-spouse Farming-fishing Husband
## 2110 Married-civ-spouse Sales Husband
## 2111 Married-civ-spouse Protective-serv Husband
## 2112 Separated Adm-clerical Unmarried
## 2113 Married-civ-spouse Adm-clerical Wife
## 2114 Never-married Sales Unmarried
## 2115 Never-married Exec-managerial Own-child
## 2116 Never-married Tech-support Own-child
## 2117 Divorced Craft-repair Not-in-family
## 2118 Divorced Prof-specialty Not-in-family
## 2119 Widowed ? Not-in-family
## 2120 Never-married Craft-repair Own-child
## 2121 Married-civ-spouse Adm-clerical Wife
## 2122 Never-married Machine-op-inspct Own-child
## 2123 Never-married Protective-serv Own-child
## 2124 Never-married Tech-support Own-child
## 2125 Never-married Priv-house-serv Own-child
## 2126 Never-married Sales Own-child
## 2127 Never-married ? Own-child
## 2128 Never-married Craft-repair Not-in-family
## 2129 Married-civ-spouse Other-service Wife
## 2130 Married-civ-spouse Prof-specialty Husband
## 2131 Separated Adm-clerical Unmarried
## 2132 Divorced Prof-specialty Unmarried
## 2133 Never-married Adm-clerical Not-in-family
## 2134 Never-married Exec-managerial Not-in-family
## 2135 Married-civ-spouse Craft-repair Husband
## 2136 Never-married Sales Not-in-family
## 2137 Never-married Priv-house-serv Not-in-family
## 2138 Divorced Adm-clerical Other-relative
## 2139 Never-married Other-service Not-in-family
## 2140 Married-civ-spouse Exec-managerial Husband
## 2141 Widowed Prof-specialty Unmarried
## 2142 Widowed Tech-support Unmarried
## 2143 Widowed Adm-clerical Unmarried
## 2144 Divorced Prof-specialty Own-child
## 2145 Married-civ-spouse Transport-moving Husband
## 2146 Married-civ-spouse Craft-repair Husband
## 2147 Never-married Prof-specialty Own-child
## 2148 Married-civ-spouse Machine-op-inspct Husband
## 2149 Divorced Prof-specialty Not-in-family
## 2150 Married-civ-spouse Craft-repair Husband
## 2151 Never-married Machine-op-inspct Not-in-family
## 2152 Never-married Adm-clerical Not-in-family
## 2153 Never-married ? Own-child
## 2154 Married-civ-spouse Prof-specialty Husband
## 2155 Widowed Priv-house-serv Not-in-family
## 2156 Married-civ-spouse ? Husband
## 2157 Married-civ-spouse Craft-repair Husband
## 2158 Married-civ-spouse Sales Husband
## 2159 Married-civ-spouse Sales Husband
## 2160 Married-civ-spouse Sales Husband
## 2161 Married-civ-spouse Exec-managerial Husband
## 2162 Never-married Other-service Not-in-family
## 2163 Never-married Craft-repair Own-child
## 2164 Married-civ-spouse ? Husband
## 2165 Married-civ-spouse ? Wife
## 2166 Never-married Other-service Own-child
## 2167 Married-civ-spouse Sales Husband
## 2168 Married-civ-spouse Exec-managerial Husband
## 2169 Divorced Sales Unmarried
## 2170 Separated Priv-house-serv Not-in-family
## 2171 Married-civ-spouse Craft-repair Husband
## 2172 Separated Protective-serv Own-child
## 2173 Married-civ-spouse Machine-op-inspct Husband
## 2174 Married-civ-spouse Transport-moving Husband
## 2175 Married-civ-spouse Sales Husband
## 2176 Married-civ-spouse Prof-specialty Wife
## 2177 Married-civ-spouse Craft-repair Husband
## 2178 Divorced Adm-clerical Not-in-family
## 2179 Married-civ-spouse Machine-op-inspct Husband
## 2180 Married-civ-spouse Exec-managerial Husband
## 2181 Married-civ-spouse Exec-managerial Wife
## 2182 Married-civ-spouse Prof-specialty Husband
## 2183 Divorced Sales Unmarried
## 2184 Married-civ-spouse Farming-fishing Husband
## 2185 Married-civ-spouse Sales Husband
## 2186 Divorced Craft-repair Other-relative
## 2187 Divorced Prof-specialty Not-in-family
## 2188 Never-married Machine-op-inspct Not-in-family
## 2189 Never-married Sales Not-in-family
## 2190 Widowed Transport-moving Not-in-family
## 2191 Married-civ-spouse Transport-moving Husband
## 2192 Never-married Adm-clerical Not-in-family
## 2193 Separated Adm-clerical Own-child
## 2194 Married-civ-spouse Handlers-cleaners Other-relative
## 2195 Married-civ-spouse Sales Husband
## 2196 Married-civ-spouse Machine-op-inspct Husband
## 2197 Married-civ-spouse Craft-repair Husband
## 2198 Never-married Adm-clerical Own-child
## 2199 Married-civ-spouse Adm-clerical Wife
## 2200 Married-civ-spouse Transport-moving Husband
## 2201 Married-civ-spouse Farming-fishing Husband
## 2202 Married-civ-spouse Adm-clerical Husband
## 2203 Widowed Adm-clerical Unmarried
## 2204 Never-married Prof-specialty Own-child
## 2205 Separated Exec-managerial Unmarried
## 2206 Married-civ-spouse Machine-op-inspct Wife
## 2207 Married-civ-spouse Transport-moving Husband
## 2208 Never-married Other-service Not-in-family
## 2209 Married-civ-spouse Sales Husband
## 2210 Widowed Protective-serv Not-in-family
## 2211 Divorced ? Not-in-family
## 2212 Never-married Other-service Not-in-family
## 2213 Married-civ-spouse Handlers-cleaners Husband
## 2214 Married-civ-spouse ? Husband
## 2215 Never-married Other-service Not-in-family
## 2216 Married-civ-spouse Sales Husband
## 2217 Never-married Adm-clerical Not-in-family
## 2218 Married-civ-spouse Exec-managerial Husband
## 2219 Never-married Machine-op-inspct Unmarried
## 2220 Divorced Sales Not-in-family
## 2221 Never-married Craft-repair Not-in-family
## 2222 Separated Other-service Not-in-family
## 2223 Never-married ? Own-child
## 2224 Never-married Craft-repair Own-child
## 2225 Never-married Transport-moving Unmarried
## 2226 Never-married Tech-support Not-in-family
## 2227 Separated Adm-clerical Unmarried
## 2228 Separated Prof-specialty Not-in-family
## 2229 Married-civ-spouse Adm-clerical Wife
## 2230 Married-civ-spouse Transport-moving Husband
## 2231 Married-civ-spouse Sales Husband
## 2232 Never-married Exec-managerial Own-child
## 2233 Never-married Sales Not-in-family
## 2234 Married-civ-spouse Sales Husband
## 2235 Married-civ-spouse Machine-op-inspct Husband
## 2236 Divorced Prof-specialty Unmarried
## 2237 Married-civ-spouse Tech-support Wife
## 2238 Married-civ-spouse Prof-specialty Husband
## 2239 Never-married Tech-support Unmarried
## 2240 Married-civ-spouse Adm-clerical Husband
## 2241 Married-civ-spouse Prof-specialty Husband
## 2242 Married-civ-spouse Craft-repair Husband
## 2243 Married-civ-spouse Handlers-cleaners Husband
## 2244 Married-civ-spouse Craft-repair Husband
## 2245 Divorced Exec-managerial Unmarried
## 2246 Married-civ-spouse Craft-repair Husband
## 2247 Divorced Sales Not-in-family
## 2248 Divorced Adm-clerical Not-in-family
## 2249 Never-married Handlers-cleaners Own-child
## 2250 Married-civ-spouse Adm-clerical Husband
## 2251 Divorced Exec-managerial Unmarried
## 2252 Never-married Adm-clerical Unmarried
## 2253 Married-civ-spouse Adm-clerical Husband
## 2254 Never-married Exec-managerial Not-in-family
## 2255 Married-civ-spouse Exec-managerial Husband
## 2256 Separated Other-service Not-in-family
## 2257 Married-civ-spouse Farming-fishing Husband
## 2258 Never-married Other-service Own-child
## 2259 Married-civ-spouse Sales Husband
## 2260 Never-married Exec-managerial Unmarried
## 2261 Never-married Transport-moving Own-child
## 2262 Married-civ-spouse Craft-repair Other-relative
## 2263 Divorced Adm-clerical Not-in-family
## 2264 Divorced Craft-repair Not-in-family
## 2265 Never-married Exec-managerial Own-child
## 2266 Married-civ-spouse Sales Husband
## 2267 Never-married Machine-op-inspct Own-child
## 2268 Never-married Transport-moving Unmarried
## 2269 Married-civ-spouse Craft-repair Husband
## 2270 Married-civ-spouse Sales Husband
## 2271 Divorced Craft-repair Unmarried
## 2272 Never-married Machine-op-inspct Not-in-family
## 2273 Never-married Sales Own-child
## 2274 Married-civ-spouse Sales Husband
## 2275 Married-civ-spouse Craft-repair Husband
## 2276 Married-civ-spouse Machine-op-inspct Husband
## 2277 Widowed Other-service Own-child
## 2278 Married-civ-spouse Craft-repair Husband
## 2279 Never-married Sales Other-relative
## 2280 Never-married Prof-specialty Not-in-family
## 2281 Never-married Prof-specialty Not-in-family
## 2282 Never-married ? Own-child
## 2283 Married-civ-spouse Other-service Husband
## 2284 Never-married Prof-specialty Unmarried
## 2285 Never-married Prof-specialty Not-in-family
## 2286 Never-married Sales Not-in-family
## 2287 Married-civ-spouse Prof-specialty Husband
## 2288 Married-civ-spouse Prof-specialty Husband
## 2289 Married-civ-spouse Protective-serv Other-relative
## 2290 Married-civ-spouse Craft-repair Husband
## 2291 Never-married Handlers-cleaners Own-child
## 2292 Divorced Craft-repair Not-in-family
## 2293 Never-married ? Own-child
## 2294 Married-civ-spouse Craft-repair Husband
## 2295 Divorced Craft-repair Unmarried
## 2296 Never-married Machine-op-inspct Own-child
## 2297 Never-married Craft-repair Other-relative
## 2298 Divorced Adm-clerical Unmarried
## 2299 Married-civ-spouse Other-service Husband
## 2300 Separated Adm-clerical Unmarried
## 2301 Divorced Exec-managerial Not-in-family
## 2302 Separated Sales Not-in-family
## 2303 Divorced Tech-support Not-in-family
## 2304 Never-married Other-service Not-in-family
## 2305 Married-civ-spouse Sales Husband
## 2306 Married-civ-spouse Other-service Wife
## 2307 Married-civ-spouse Craft-repair Husband
## 2308 Never-married Handlers-cleaners Own-child
## 2309 Married-civ-spouse Prof-specialty Husband
## 2310 Widowed Adm-clerical Not-in-family
## 2311 Widowed Transport-moving Not-in-family
## 2312 Married-spouse-absent Prof-specialty Not-in-family
## 2313 Married-civ-spouse Sales Wife
## 2314 Never-married Adm-clerical Own-child
## 2315 Married-civ-spouse Adm-clerical Wife
## 2316 Widowed Craft-repair Not-in-family
## 2317 Married-civ-spouse Exec-managerial Husband
## 2318 Never-married Sales Not-in-family
## 2319 Married-civ-spouse Prof-specialty Husband
## 2320 Married-civ-spouse Sales Husband
## 2321 Never-married Craft-repair Not-in-family
## 2322 Married-civ-spouse Protective-serv Husband
## 2323 Never-married Craft-repair Own-child
## 2324 Never-married ? Own-child
## 2325 Married-civ-spouse Exec-managerial Husband
## 2326 Married-civ-spouse Exec-managerial Husband
## 2327 Widowed Craft-repair Unmarried
## 2328 Married-spouse-absent ? Not-in-family
## 2329 Married-civ-spouse Farming-fishing Husband
## 2330 Divorced Sales Not-in-family
## 2331 Married-civ-spouse Exec-managerial Wife
## 2332 Divorced Prof-specialty Not-in-family
## 2333 Married-civ-spouse Transport-moving Husband
## 2334 Separated Transport-moving Not-in-family
## 2335 Married-civ-spouse Exec-managerial Husband
## 2336 Never-married Adm-clerical Own-child
## 2337 Married-spouse-absent Machine-op-inspct Not-in-family
## 2338 Never-married Transport-moving Own-child
## 2339 Never-married Tech-support Own-child
## 2340 Married-civ-spouse Exec-managerial Husband
## 2341 Divorced ? Not-in-family
## 2342 Married-civ-spouse Craft-repair Husband
## 2343 Never-married Farming-fishing Not-in-family
## 2344 Never-married Other-service Unmarried
## 2345 Married-civ-spouse Exec-managerial Husband
## 2346 Married-civ-spouse Sales Husband
## 2347 Married-civ-spouse Tech-support Husband
## 2348 Never-married Handlers-cleaners Own-child
## 2349 Married-civ-spouse Craft-repair Husband
## 2350 Never-married Exec-managerial Not-in-family
## 2351 Divorced Exec-managerial Not-in-family
## 2352 Never-married Exec-managerial Not-in-family
## 2353 Married-civ-spouse Exec-managerial Husband
## 2354 Married-civ-spouse Farming-fishing Husband
## 2355 Never-married ? Own-child
## 2356 Married-civ-spouse Sales Husband
## 2357 Separated ? Own-child
## 2358 Married-civ-spouse Other-service Husband
## 2359 Married-civ-spouse ? Husband
## 2360 Never-married ? Own-child
## 2361 Married-civ-spouse Exec-managerial Husband
## 2362 Married-civ-spouse Prof-specialty Husband
## 2363 Married-civ-spouse Craft-repair Husband
## 2364 Never-married Handlers-cleaners Own-child
## 2365 Never-married Transport-moving Own-child
## 2366 Separated Other-service Not-in-family
## 2367 Never-married Adm-clerical Not-in-family
## 2368 Separated Prof-specialty Not-in-family
## 2369 Never-married Craft-repair Own-child
## 2370 Married-civ-spouse Handlers-cleaners Husband
## 2371 Married-civ-spouse Protective-serv Husband
## 2372 Separated Prof-specialty Not-in-family
## 2373 Married-civ-spouse ? Husband
## 2374 Married-civ-spouse Craft-repair Husband
## 2375 Never-married Prof-specialty Not-in-family
## 2376 Divorced Sales Not-in-family
## 2377 Never-married Sales Not-in-family
## 2378 Married-civ-spouse Machine-op-inspct Husband
## 2379 Separated Adm-clerical Unmarried
## 2380 Divorced Exec-managerial Not-in-family
## 2381 Never-married ? Not-in-family
## 2382 Never-married Craft-repair Own-child
## 2383 Never-married ? Not-in-family
## 2384 Married-spouse-absent Other-service Unmarried
## 2385 Never-married Exec-managerial Own-child
## 2386 Married-civ-spouse Sales Husband
## 2387 Married-civ-spouse Sales Husband
## 2388 Married-civ-spouse Farming-fishing Husband
## 2389 Married-civ-spouse Transport-moving Husband
## 2390 Married-civ-spouse Adm-clerical Husband
## 2391 Married-civ-spouse Exec-managerial Husband
## 2392 Never-married Prof-specialty Not-in-family
## 2393 Married-civ-spouse Handlers-cleaners Husband
## 2394 Never-married Handlers-cleaners Not-in-family
## 2395 Never-married Sales Own-child
## 2396 Never-married Machine-op-inspct Not-in-family
## 2397 Never-married Prof-specialty Not-in-family
## 2398 Never-married ? Own-child
## 2399 Never-married Sales Own-child
## 2400 Divorced Machine-op-inspct Not-in-family
## 2401 Never-married Adm-clerical Not-in-family
## 2402 Never-married Machine-op-inspct Own-child
## 2403 Married-civ-spouse Other-service Husband
## 2404 Married-civ-spouse Exec-managerial Husband
## 2405 Married-civ-spouse Sales Husband
## 2406 Married-civ-spouse Machine-op-inspct Husband
## 2407 Married-civ-spouse Exec-managerial Husband
## 2408 Married-civ-spouse Other-service Wife
## 2409 Never-married Adm-clerical Not-in-family
## 2410 Never-married Other-service Own-child
## 2411 Married-civ-spouse Other-service Husband
## 2412 Divorced Adm-clerical Unmarried
## 2413 Never-married Exec-managerial Own-child
## 2414 Never-married Farming-fishing Not-in-family
## 2415 Divorced Exec-managerial Unmarried
## 2416 Married-civ-spouse Prof-specialty Husband
## 2417 Married-civ-spouse Farming-fishing Husband
## 2418 Never-married Adm-clerical Own-child
## 2419 Never-married Sales Not-in-family
## 2420 Married-civ-spouse Craft-repair Husband
## 2421 Married-civ-spouse ? Husband
## 2422 Widowed Adm-clerical Not-in-family
## 2423 Married-civ-spouse Transport-moving Husband
## 2424 Never-married Sales Not-in-family
## 2425 Married-civ-spouse Machine-op-inspct Husband
## 2426 Married-civ-spouse Machine-op-inspct Husband
## 2427 Married-civ-spouse Prof-specialty Husband
## 2428 Never-married ? Own-child
## 2429 Never-married Sales Own-child
## 2430 Married-civ-spouse Farming-fishing Husband
## 2431 Married-civ-spouse Craft-repair Husband
## 2432 Married-civ-spouse Craft-repair Husband
## 2433 Married-civ-spouse Sales Husband
## 2434 Married-civ-spouse Exec-managerial Husband
## 2435 Never-married Tech-support Not-in-family
## 2436 Never-married Other-service Unmarried
## 2437 Never-married Adm-clerical Not-in-family
## 2438 Never-married Other-service Not-in-family
## 2439 Never-married Tech-support Own-child
## 2440 Married-civ-spouse Craft-repair Husband
## 2441 Married-civ-spouse Sales Husband
## 2442 Married-civ-spouse Machine-op-inspct Husband
## 2443 Divorced Adm-clerical Unmarried
## 2444 Never-married Craft-repair Other-relative
## 2445 Never-married Sales Own-child
## 2446 Married-civ-spouse Prof-specialty Husband
## 2447 Married-civ-spouse Handlers-cleaners Husband
## 2448 Divorced Other-service Not-in-family
## 2449 Never-married Protective-serv Not-in-family
## 2450 Separated Other-service Own-child
## 2451 Divorced Sales Not-in-family
## 2452 Never-married Other-service Unmarried
## 2453 Never-married Sales Not-in-family
## 2454 Married-civ-spouse Adm-clerical Wife
## 2455 Divorced Adm-clerical Unmarried
## 2456 Divorced Machine-op-inspct Not-in-family
## 2457 Married-civ-spouse Sales Husband
## 2458 Married-civ-spouse Craft-repair Husband
## 2459 Separated Adm-clerical Unmarried
## 2460 Divorced Craft-repair Own-child
## 2461 Married-civ-spouse Craft-repair Husband
## 2462 Never-married Craft-repair Not-in-family
## 2463 Married-civ-spouse Exec-managerial Husband
## 2464 Divorced Protective-serv Not-in-family
## 2465 Married-civ-spouse ? Husband
## 2466 Married-civ-spouse Sales Husband
## 2467 Divorced Machine-op-inspct Not-in-family
## 2468 Divorced Sales Other-relative
## 2469 Divorced Sales Not-in-family
## 2470 Married-civ-spouse Sales Wife
## 2471 Married-civ-spouse Tech-support Husband
## 2472 Never-married Prof-specialty Not-in-family
## 2473 Married-civ-spouse Tech-support Other-relative
## 2474 Married-civ-spouse Sales Husband
## 2475 Married-civ-spouse Craft-repair Husband
## 2476 Never-married Transport-moving Own-child
## 2477 Never-married ? Not-in-family
## 2478 Separated Craft-repair Not-in-family
## 2479 Married-civ-spouse Machine-op-inspct Husband
## 2480 Separated Handlers-cleaners Unmarried
## 2481 Married-civ-spouse Sales Husband
## 2482 Married-civ-spouse Exec-managerial Husband
## 2483 Never-married Adm-clerical Not-in-family
## 2484 Married-civ-spouse Exec-managerial Husband
## 2485 Married-civ-spouse Prof-specialty Husband
## 2486 Married-civ-spouse Craft-repair Husband
## 2487 Married-civ-spouse ? Husband
## 2488 Divorced Adm-clerical Not-in-family
## 2489 Married-civ-spouse Craft-repair Husband
## 2490 Divorced Sales Unmarried
## 2491 Divorced Craft-repair Unmarried
## 2492 Married-civ-spouse ? Husband
## 2493 Never-married Handlers-cleaners Own-child
## 2494 Never-married Craft-repair Own-child
## 2495 Divorced Exec-managerial Not-in-family
## 2496 Married-civ-spouse Transport-moving Husband
## 2497 Never-married ? Own-child
## 2498 Married-civ-spouse Exec-managerial Husband
## 2499 Married-civ-spouse Sales Husband
## 2500 Never-married Protective-serv Own-child
## 2501 Never-married Adm-clerical Not-in-family
## 2502 Married-spouse-absent Exec-managerial Not-in-family
## 2503 Divorced Machine-op-inspct Not-in-family
## 2504 Divorced Craft-repair Unmarried
## 2505 Married-civ-spouse Transport-moving Husband
## 2506 Widowed Craft-repair Not-in-family
## 2507 Never-married ? Own-child
## 2508 Married-civ-spouse Sales Husband
## 2509 Never-married Craft-repair Not-in-family
## 2510 Married-civ-spouse Craft-repair Not-in-family
## 2511 Divorced Protective-serv Not-in-family
## 2512 Never-married Adm-clerical Not-in-family
## 2513 Married-civ-spouse Adm-clerical Husband
## 2514 Married-civ-spouse ? Husband
## 2515 Married-civ-spouse Transport-moving Husband
## 2516 Never-married Handlers-cleaners Other-relative
## 2517 Married-civ-spouse Other-service Wife
## 2518 Married-civ-spouse Exec-managerial Husband
## 2519 Never-married Other-service Own-child
## 2520 Married-civ-spouse Craft-repair Husband
## 2521 Married-civ-spouse Craft-repair Husband
## 2522 Never-married ? Not-in-family
## 2523 Married-civ-spouse Craft-repair Husband
## 2524 Separated Other-service Unmarried
## 2525 Married-civ-spouse Machine-op-inspct Husband
## 2526 Divorced Machine-op-inspct Not-in-family
## 2527 Divorced Prof-specialty Unmarried
## 2528 Never-married Exec-managerial Other-relative
## 2529 Married-civ-spouse Exec-managerial Husband
## 2530 Never-married Exec-managerial Not-in-family
## 2531 Never-married Other-service Not-in-family
## 2532 Never-married Other-service Not-in-family
## 2533 Never-married Protective-serv Own-child
## 2534 Never-married Sales Not-in-family
## 2535 Married-civ-spouse Craft-repair Husband
## 2536 Divorced Adm-clerical Unmarried
## 2537 Never-married Adm-clerical Not-in-family
## 2538 Never-married Sales Not-in-family
## 2539 Married-civ-spouse Machine-op-inspct Husband
## 2540 Separated Exec-managerial Unmarried
## 2541 Widowed Adm-clerical Unmarried
## 2542 Married-civ-spouse Adm-clerical Husband
## 2543 Married-spouse-absent Sales Not-in-family
## 2544 Widowed Machine-op-inspct Unmarried
## 2545 Never-married ? Own-child
## 2546 Divorced Sales Not-in-family
## 2547 Married-civ-spouse Transport-moving Husband
## 2548 Never-married Adm-clerical Own-child
## 2549 Divorced Adm-clerical Not-in-family
## 2550 Married-spouse-absent Prof-specialty Not-in-family
## 2551 Never-married Other-service Own-child
## 2552 Married-civ-spouse Farming-fishing Husband
## 2553 Married-civ-spouse Sales Husband
## 2554 Never-married Adm-clerical Unmarried
## 2555 Married-civ-spouse Exec-managerial Husband
## 2556 Never-married Farming-fishing Own-child
## 2557 Married-civ-spouse Sales Husband
## 2558 Never-married Prof-specialty Not-in-family
## 2559 Married-civ-spouse Prof-specialty Husband
## 2560 Married-civ-spouse Adm-clerical Wife
## 2561 Married-civ-spouse Adm-clerical Wife
## 2562 Never-married Machine-op-inspct Unmarried
## 2563 Divorced Exec-managerial Not-in-family
## 2564 Never-married Adm-clerical Unmarried
## 2565 Never-married Sales Own-child
## 2566 Married-civ-spouse Adm-clerical Husband
## 2567 Married-civ-spouse ? Husband
## 2568 Never-married Prof-specialty Not-in-family
## 2569 Never-married Farming-fishing Own-child
## 2570 Married-civ-spouse Exec-managerial Husband
## 2571 Married-civ-spouse ? Husband
## 2572 Never-married ? Not-in-family
## 2573 Separated Craft-repair Not-in-family
## 2574 Divorced Machine-op-inspct Not-in-family
## 2575 Married-civ-spouse Prof-specialty Husband
## 2576 Never-married Sales Unmarried
## 2577 Married-civ-spouse Sales Husband
## 2578 Divorced Adm-clerical Unmarried
## 2579 Married-civ-spouse ? Husband
## 2580 Married-civ-spouse Transport-moving Husband
## 2581 Married-civ-spouse Transport-moving Husband
## 2582 Married-spouse-absent Machine-op-inspct Unmarried
## 2583 Married-civ-spouse Craft-repair Husband
## 2584 Married-civ-spouse Exec-managerial Husband
## 2585 Divorced Craft-repair Unmarried
## 2586 Divorced Adm-clerical Not-in-family
## 2587 Never-married ? Not-in-family
## 2588 Married-civ-spouse Prof-specialty Husband
## 2589 Married-civ-spouse Exec-managerial Husband
## 2590 Married-civ-spouse Craft-repair Husband
## 2591 Divorced Adm-clerical Other-relative
## 2592 Married-spouse-absent Craft-repair Not-in-family
## 2593 Never-married Machine-op-inspct Not-in-family
## 2594 Never-married Exec-managerial Not-in-family
## 2595 Widowed ? Unmarried
## 2596 Divorced Handlers-cleaners Unmarried
## 2597 Never-married Craft-repair Own-child
## 2598 Divorced Prof-specialty Not-in-family
## 2599 Married-civ-spouse Transport-moving Husband
## 2600 Married-civ-spouse Adm-clerical Husband
## 2601 Never-married Adm-clerical Not-in-family
## 2602 Married-civ-spouse Transport-moving Husband
## 2603 Divorced Tech-support Unmarried
## 2604 Divorced Exec-managerial Not-in-family
## 2605 Never-married Handlers-cleaners Own-child
## 2606 Married-civ-spouse Other-service Husband
## 2607 Never-married ? Own-child
## 2608 Married-civ-spouse Exec-managerial Husband
## 2609 Never-married Other-service Not-in-family
## 2610 Married-civ-spouse Exec-managerial Husband
## 2611 Never-married Sales Other-relative
## 2612 Married-civ-spouse Transport-moving Husband
## 2613 Married-civ-spouse Sales Husband
## 2614 Never-married Machine-op-inspct Other-relative
## 2615 Married-civ-spouse Exec-managerial Husband
## 2616 Widowed Adm-clerical Not-in-family
## 2617 Never-married Transport-moving Not-in-family
## 2618 Never-married Adm-clerical Not-in-family
## 2619 Married-civ-spouse Sales Husband
## 2620 Married-civ-spouse Handlers-cleaners Husband
## 2621 Married-civ-spouse Transport-moving Husband
## 2622 Never-married Sales Not-in-family
## 2623 Married-civ-spouse Craft-repair Husband
## 2624 Never-married Craft-repair Not-in-family
## 2625 Married-civ-spouse Craft-repair Husband
## 2626 Married-civ-spouse Craft-repair Husband
## 2627 Married-civ-spouse Exec-managerial Husband
## 2628 Divorced Craft-repair Not-in-family
## 2629 Never-married Handlers-cleaners Other-relative
## 2630 Married-civ-spouse Craft-repair Husband
## 2631 Never-married Tech-support Not-in-family
## 2632 Married-civ-spouse Tech-support Husband
## 2633 Married-civ-spouse ? Wife
## 2634 Married-civ-spouse Sales Husband
## 2635 Never-married ? Not-in-family
## 2636 Married-civ-spouse Prof-specialty Husband
## 2637 Never-married Adm-clerical Not-in-family
## 2638 Married-civ-spouse Exec-managerial Wife
## 2639 Married-civ-spouse ? Husband
## 2640 Never-married Machine-op-inspct Not-in-family
## 2641 Married-civ-spouse Craft-repair Husband
## 2642 Divorced Prof-specialty Not-in-family
## 2643 Never-married Adm-clerical Own-child
## 2644 Married-civ-spouse Craft-repair Husband
## 2645 Never-married Other-service Not-in-family
## 2646 Married-civ-spouse Craft-repair Husband
## 2647 Widowed Other-service Not-in-family
## 2648 Married-civ-spouse Sales Husband
## 2649 Never-married Adm-clerical Not-in-family
## 2650 Married-civ-spouse Adm-clerical Wife
## 2651 Never-married Adm-clerical Own-child
## 2652 Married-spouse-absent Exec-managerial Not-in-family
## 2653 Married-civ-spouse Prof-specialty Husband
## 2654 Never-married Adm-clerical Not-in-family
## 2655 Married-civ-spouse Exec-managerial Husband
## 2656 Never-married Adm-clerical Other-relative
## 2657 Never-married Sales Own-child
## 2658 Widowed Adm-clerical Unmarried
## 2659 Married-civ-spouse Transport-moving Husband
## 2660 Married-civ-spouse Prof-specialty Husband
## 2661 Married-civ-spouse Craft-repair Husband
## 2662 Married-civ-spouse Craft-repair Husband
## 2663 Separated Machine-op-inspct Unmarried
## 2664 Divorced Prof-specialty Unmarried
## 2665 Divorced Exec-managerial Not-in-family
## 2666 Married-civ-spouse Sales Husband
## 2667 Never-married Handlers-cleaners Own-child
## 2668 Married-civ-spouse Other-service Husband
## 2669 Separated Other-service Own-child
## 2670 Widowed Exec-managerial Not-in-family
## 2671 Married-civ-spouse Handlers-cleaners Husband
## 2672 Never-married Sales Own-child
## 2673 Never-married Prof-specialty Not-in-family
## 2674 Never-married ? Own-child
## 2675 Never-married Other-service Not-in-family
## 2676 Married-civ-spouse Craft-repair Husband
## 2677 Married-civ-spouse ? Husband
## 2678 Never-married Adm-clerical Unmarried
## 2679 Never-married Farming-fishing Not-in-family
## 2680 Widowed Tech-support Unmarried
## 2681 Married-civ-spouse Prof-specialty Husband
## 2682 Married-civ-spouse Prof-specialty Husband
## 2683 Married-spouse-absent Prof-specialty Not-in-family
## 2684 Married-civ-spouse Tech-support Husband
## 2685 Married-civ-spouse Exec-managerial Husband
## 2686 Never-married Adm-clerical Unmarried
## 2687 Divorced Prof-specialty Not-in-family
## 2688 Married-civ-spouse Prof-specialty Husband
## 2689 Married-civ-spouse Transport-moving Husband
## 2690 Never-married ? Other-relative
## 2691 Married-civ-spouse Craft-repair Husband
## 2692 Married-civ-spouse Adm-clerical Husband
## 2693 Married-civ-spouse Other-service Husband
## 2694 Never-married Exec-managerial Not-in-family
## 2695 Married-civ-spouse Craft-repair Husband
## 2696 Married-civ-spouse Transport-moving Husband
## 2697 Divorced Other-service Own-child
## 2698 Married-civ-spouse Sales Husband
## 2699 Divorced Farming-fishing Not-in-family
## 2700 Divorced Prof-specialty Not-in-family
## 2701 Divorced Sales Own-child
## 2702 Never-married Adm-clerical Not-in-family
## 2703 Married-civ-spouse Sales Husband
## 2704 Never-married Other-service Own-child
## 2705 Never-married Other-service Own-child
## 2706 Divorced Tech-support Unmarried
## 2707 Married-civ-spouse Handlers-cleaners Other-relative
## 2708 Married-civ-spouse Prof-specialty Husband
## 2709 Divorced Sales Not-in-family
## 2710 Never-married Adm-clerical Own-child
## 2711 Married-civ-spouse Tech-support Husband
## 2712 Never-married Machine-op-inspct Other-relative
## 2713 Never-married Other-service Not-in-family
## 2714 Divorced Exec-managerial Not-in-family
## 2715 Never-married Craft-repair Own-child
## 2716 Widowed Adm-clerical Not-in-family
## 2717 Married-civ-spouse Prof-specialty Wife
## 2718 Divorced Sales Unmarried
## 2719 Divorced Exec-managerial Not-in-family
## 2720 Married-civ-spouse Sales Husband
## 2721 Never-married Adm-clerical Unmarried
## 2722 Married-civ-spouse Sales Husband
## 2723 Never-married Handlers-cleaners Own-child
## 2724 Never-married Exec-managerial Own-child
## 2725 Divorced Prof-specialty Unmarried
## 2726 Never-married Prof-specialty Not-in-family
## 2727 Never-married Prof-specialty Not-in-family
## 2728 Never-married Adm-clerical Not-in-family
## 2729 Never-married Other-service Own-child
## 2730 Never-married Other-service Own-child
## 2731 Married-civ-spouse Priv-house-serv Other-relative
## 2732 Married-civ-spouse Prof-specialty Husband
## 2733 Never-married Other-service Not-in-family
## 2734 Divorced Exec-managerial Unmarried
## 2735 Married-civ-spouse Craft-repair Husband
## 2736 Never-married Prof-specialty Not-in-family
## 2737 Married-civ-spouse Protective-serv Husband
## 2738 Divorced Sales Other-relative
## 2739 Married-civ-spouse Adm-clerical Wife
## 2740 Never-married Exec-managerial Not-in-family
## 2741 Married-civ-spouse Exec-managerial Husband
## 2742 Married-civ-spouse Other-service Husband
## 2743 Divorced Other-service Unmarried
## 2744 Married-civ-spouse Adm-clerical Husband
## 2745 Married-civ-spouse Sales Husband
## 2746 Married-civ-spouse Machine-op-inspct Husband
## 2747 Divorced Exec-managerial Other-relative
## 2748 Married-civ-spouse Machine-op-inspct Husband
## 2749 Married-civ-spouse Transport-moving Husband
## 2750 Never-married Machine-op-inspct Own-child
## 2751 Divorced Sales Not-in-family
## 2752 Divorced ? Not-in-family
## 2753 Married-civ-spouse Exec-managerial Husband
## 2754 Married-civ-spouse Sales Husband
## 2755 Married-civ-spouse Farming-fishing Husband
## 2756 Never-married Other-service Own-child
## 2757 Married-civ-spouse Exec-managerial Husband
## 2758 Never-married Handlers-cleaners Own-child
## 2759 Separated Exec-managerial Not-in-family
## 2760 Married-civ-spouse Exec-managerial Husband
## 2761 Married-civ-spouse ? Husband
## 2762 Divorced ? Own-child
## 2763 Married-civ-spouse Craft-repair Husband
## 2764 Never-married Adm-clerical Not-in-family
## 2765 Never-married Other-service Own-child
## 2766 Divorced Adm-clerical Unmarried
## 2767 Never-married Craft-repair Own-child
## 2768 Married-civ-spouse Transport-moving Husband
## 2769 Divorced Sales Not-in-family
## 2770 Never-married Protective-serv Own-child
## 2771 Divorced Adm-clerical Unmarried
## 2772 Divorced Exec-managerial Not-in-family
## 2773 Never-married Other-service Own-child
## 2774 Never-married Other-service Not-in-family
## 2775 Married-civ-spouse Tech-support Husband
## 2776 Married-civ-spouse Prof-specialty Husband
## 2777 Never-married Sales Own-child
## 2778 Divorced Transport-moving Not-in-family
## 2779 Separated Adm-clerical Not-in-family
## 2780 Divorced Other-service Not-in-family
## 2781 Married-civ-spouse Prof-specialty Husband
## 2782 Married-civ-spouse Sales Husband
## 2783 Never-married Sales Own-child
## 2784 Divorced Protective-serv Not-in-family
## 2785 Never-married Handlers-cleaners Not-in-family
## 2786 Widowed Adm-clerical Unmarried
## 2787 Married-civ-spouse Sales Husband
## 2788 Never-married Sales Not-in-family
## 2789 Never-married Prof-specialty Not-in-family
## 2790 Divorced Machine-op-inspct Not-in-family
## 2791 Widowed Adm-clerical Not-in-family
## 2792 Divorced Adm-clerical Not-in-family
## 2793 Divorced Craft-repair Not-in-family
## 2794 Widowed Sales Not-in-family
## 2795 Married-civ-spouse Prof-specialty Husband
## 2796 Divorced Machine-op-inspct Unmarried
## 2797 Married-civ-spouse Prof-specialty Husband
## 2798 Never-married Adm-clerical Not-in-family
## 2799 Never-married Other-service Unmarried
## 2800 Married-civ-spouse Prof-specialty Husband
## 2801 Married-civ-spouse Prof-specialty Husband
## 2802 Never-married Other-service Own-child
## 2803 Married-civ-spouse Prof-specialty Husband
## 2804 Never-married Prof-specialty Own-child
## 2805 Married-civ-spouse Adm-clerical Wife
## 2806 Never-married Sales Own-child
## 2807 Separated Prof-specialty Not-in-family
## 2808 Widowed Other-service Unmarried
## 2809 Divorced Other-service Unmarried
## 2810 Never-married Adm-clerical Not-in-family
## 2811 Married-civ-spouse Farming-fishing Husband
## 2812 Widowed Other-service Unmarried
## 2813 Married-civ-spouse Sales Wife
## 2814 Never-married Adm-clerical Not-in-family
## 2815 Never-married Sales Own-child
## 2816 Married-civ-spouse Transport-moving Husband
## 2817 Married-civ-spouse Craft-repair Husband
## 2818 Married-civ-spouse Machine-op-inspct Husband
## 2819 Never-married Prof-specialty Not-in-family
## 2820 Never-married Other-service Not-in-family
## 2821 Married-civ-spouse Exec-managerial Husband
## 2822 Never-married Machine-op-inspct Own-child
## 2823 Divorced Craft-repair Own-child
## 2824 Never-married Prof-specialty Own-child
## 2825 Married-civ-spouse Prof-specialty Husband
## 2826 Married-civ-spouse Exec-managerial Wife
## 2827 Married-civ-spouse Craft-repair Husband
## 2828 Never-married Machine-op-inspct Not-in-family
## 2829 Married-civ-spouse Exec-managerial Husband
## 2830 Married-civ-spouse Prof-specialty Husband
## 2831 Married-civ-spouse Exec-managerial Husband
## 2832 Married-civ-spouse Exec-managerial Husband
## 2833 Married-civ-spouse Sales Husband
## 2834 Married-civ-spouse Prof-specialty Husband
## 2835 Never-married Adm-clerical Not-in-family
## 2836 Never-married Sales Own-child
## 2837 Divorced Craft-repair Not-in-family
## 2838 Married-civ-spouse Other-service Wife
## 2839 Divorced Sales Not-in-family
## 2840 Married-civ-spouse Craft-repair Husband
## 2841 Never-married Prof-specialty Not-in-family
## 2842 Divorced Prof-specialty Unmarried
## 2843 Married-civ-spouse Adm-clerical Husband
## 2844 Married-civ-spouse Other-service Husband
## 2845 Married-civ-spouse Sales Husband
## 2846 Married-civ-spouse Sales Husband
## 2847 Married-civ-spouse Prof-specialty Husband
## 2848 Never-married ? Unmarried
## 2849 Married-civ-spouse Exec-managerial Husband
## 2850 Never-married Other-service Own-child
## 2851 Married-civ-spouse Farming-fishing Husband
## 2852 Never-married Prof-specialty Own-child
## 2853 Married-civ-spouse Sales Wife
## 2854 Separated Sales Other-relative
## 2855 Never-married Prof-specialty Not-in-family
## 2856 Never-married Other-service Unmarried
## 2857 Married-civ-spouse ? Husband
## 2858 Divorced ? Unmarried
## 2859 Never-married ? Own-child
## 2860 Never-married Prof-specialty Not-in-family
## 2861 Married-civ-spouse Sales Husband
## 2862 Never-married Machine-op-inspct Not-in-family
## 2863 Never-married Handlers-cleaners Unmarried
## 2864 Married-civ-spouse Prof-specialty Husband
## 2865 Married-civ-spouse Sales Wife
## 2866 Divorced Machine-op-inspct Unmarried
## 2867 Married-civ-spouse Handlers-cleaners Husband
## 2868 Divorced Other-service Unmarried
## 2869 Never-married Prof-specialty Own-child
## 2870 Never-married Handlers-cleaners Own-child
## 2871 Married-civ-spouse Craft-repair Husband
## 2872 Married-civ-spouse Protective-serv Husband
## 2873 Married-civ-spouse Craft-repair Husband
## 2874 Divorced Prof-specialty Unmarried
## 2875 Married-civ-spouse Adm-clerical Wife
## 2876 Never-married Protective-serv Other-relative
## 2877 Never-married Craft-repair Not-in-family
## 2878 Married-civ-spouse Craft-repair Husband
## 2879 Married-civ-spouse Handlers-cleaners Husband
## 2880 Never-married Other-service Other-relative
## 2881 Never-married Handlers-cleaners Other-relative
## 2882 Separated Adm-clerical Not-in-family
## 2883 Married-civ-spouse Exec-managerial Husband
## 2884 Divorced Exec-managerial Not-in-family
## 2885 Widowed Craft-repair Unmarried
## 2886 Widowed ? Unmarried
## 2887 Married-civ-spouse Craft-repair Husband
## 2888 Married-AF-spouse Sales Wife
## 2889 Divorced Prof-specialty Unmarried
## 2890 Married-civ-spouse Adm-clerical Husband
## 2891 Married-spouse-absent Other-service Own-child
## 2892 Separated Adm-clerical Own-child
## 2893 Married-civ-spouse Exec-managerial Husband
## 2894 Never-married Prof-specialty Not-in-family
## 2895 Married-civ-spouse Exec-managerial Husband
## 2896 Separated Adm-clerical Unmarried
## 2897 Married-civ-spouse Machine-op-inspct Husband
## 2898 Separated Other-service Not-in-family
## 2899 Never-married Prof-specialty Own-child
## 2900 Divorced Tech-support Unmarried
## 2901 Married-civ-spouse Transport-moving Husband
## 2902 Married-civ-spouse Exec-managerial Husband
## 2903 Married-civ-spouse Handlers-cleaners Husband
## 2904 Married-civ-spouse Sales Husband
## 2905 Married-civ-spouse Other-service Husband
## 2906 Divorced Other-service Unmarried
## 2907 Widowed Priv-house-serv Not-in-family
## 2908 Divorced Adm-clerical Own-child
## 2909 Never-married Other-service Own-child
## 2910 Married-civ-spouse Exec-managerial Husband
## 2911 Never-married Handlers-cleaners Own-child
## 2912 Never-married Sales Own-child
## 2913 Separated Adm-clerical Unmarried
## 2914 Never-married Adm-clerical Not-in-family
## 2915 Never-married Machine-op-inspct Unmarried
## 2916 Married-civ-spouse Sales Wife
## 2917 Divorced Sales Unmarried
## 2918 Married-civ-spouse Tech-support Husband
## 2919 Married-civ-spouse Exec-managerial Husband
## 2920 Married-civ-spouse Sales Husband
## 2921 Married-civ-spouse Farming-fishing Husband
## 2922 Never-married Craft-repair Other-relative
## 2923 Divorced Craft-repair Unmarried
## 2924 Married-civ-spouse Exec-managerial Husband
## 2925 Never-married Craft-repair Not-in-family
## 2926 Married-civ-spouse Sales Wife
## 2927 Married-spouse-absent Craft-repair Not-in-family
## 2928 Divorced Machine-op-inspct Own-child
## 2929 Never-married Adm-clerical Own-child
## 2930 Married-civ-spouse Transport-moving Husband
## 2931 Never-married ? Own-child
## 2932 Never-married Craft-repair Not-in-family
## 2933 Never-married ? Unmarried
## 2934 Never-married Prof-specialty Own-child
## 2935 Never-married Machine-op-inspct Own-child
## 2936 Married-civ-spouse Adm-clerical Husband
## 2937 Never-married Other-service Own-child
## 2938 Never-married Other-service Own-child
## 2939 Married-civ-spouse Machine-op-inspct Wife
## 2940 Married-civ-spouse Farming-fishing Husband
## 2941 Never-married Sales Unmarried
## 2942 Married-civ-spouse Exec-managerial Husband
## 2943 Married-civ-spouse Craft-repair Husband
## 2944 Never-married Tech-support Own-child
## 2945 Never-married Other-service Own-child
## 2946 Never-married Craft-repair Own-child
## 2947 Never-married Other-service Other-relative
## 2948 Never-married ? Own-child
## 2949 Married-civ-spouse Prof-specialty Husband
## 2950 Never-married Craft-repair Own-child
## 2951 Married-civ-spouse Prof-specialty Husband
## 2952 Widowed Exec-managerial Unmarried
## 2953 Married-civ-spouse ? Wife
## 2954 Married-civ-spouse Protective-serv Husband
## 2955 Never-married Machine-op-inspct Unmarried
## 2956 Married-civ-spouse Prof-specialty Husband
## 2957 Married-civ-spouse Exec-managerial Husband
## 2958 Separated Prof-specialty Unmarried
## 2959 Divorced Exec-managerial Not-in-family
## 2960 Married-civ-spouse Farming-fishing Husband
## 2961 Never-married Prof-specialty Not-in-family
## 2962 Widowed ? Not-in-family
## 2963 Married-civ-spouse Prof-specialty Husband
## 2964 Never-married Farming-fishing Own-child
## 2965 Never-married Craft-repair Own-child
## 2966 Married-civ-spouse Exec-managerial Husband
## 2967 Never-married Craft-repair Not-in-family
## 2968 Widowed Adm-clerical Not-in-family
## 2969 Married-civ-spouse Sales Husband
## 2970 Never-married Sales Not-in-family
## 2971 Never-married Farming-fishing Not-in-family
## 2972 Married-civ-spouse Prof-specialty Husband
## 2973 Divorced Handlers-cleaners Own-child
## 2974 Married-civ-spouse Sales Husband
## 2975 Married-civ-spouse Craft-repair Husband
## 2976 Never-married Adm-clerical Unmarried
## 2977 Divorced Craft-repair Not-in-family
## 2978 Married-civ-spouse Sales Husband
## 2979 Divorced Machine-op-inspct Unmarried
## 2980 Divorced Adm-clerical Unmarried
## 2981 Never-married Exec-managerial Not-in-family
## 2982 Never-married Handlers-cleaners Other-relative
## 2983 Married-civ-spouse Exec-managerial Husband
## 2984 Married-civ-spouse Farming-fishing Husband
## 2985 Divorced Adm-clerical Unmarried
## 2986 Never-married Other-service Own-child
## 2987 Married-civ-spouse Craft-repair Husband
## 2988 Never-married Handlers-cleaners Not-in-family
## 2989 Never-married Craft-repair Own-child
## 2990 Married-civ-spouse Exec-managerial Husband
## 2991 Never-married Prof-specialty Own-child
## 2992 Divorced Sales Not-in-family
## 2993 Never-married Exec-managerial Unmarried
## 2994 Married-civ-spouse Farming-fishing Wife
## 2995 Married-civ-spouse Sales Husband
## 2996 Married-civ-spouse Transport-moving Husband
## 2997 Never-married Exec-managerial Not-in-family
## 2998 Divorced Machine-op-inspct Other-relative
## 2999 Married-civ-spouse Handlers-cleaners Husband
## 3000 Married-civ-spouse ? Husband
## 3001 Married-civ-spouse Machine-op-inspct Husband
## 3002 Married-civ-spouse Craft-repair Husband
## 3003 Separated Other-service Unmarried
## 3004 Divorced Sales Not-in-family
## 3005 Never-married Adm-clerical Not-in-family
## 3006 Never-married ? Own-child
## 3007 Divorced Tech-support Unmarried
## 3008 Divorced Tech-support Other-relative
## 3009 Never-married Craft-repair Other-relative
## 3010 Married-civ-spouse Craft-repair Husband
## 3011 Married-civ-spouse Sales Wife
## 3012 Separated Exec-managerial Not-in-family
## 3013 Married-civ-spouse Prof-specialty Husband
## 3014 Never-married Prof-specialty Not-in-family
## 3015 Married-civ-spouse Handlers-cleaners Husband
## 3016 Never-married Exec-managerial Not-in-family
## 3017 Married-civ-spouse Adm-clerical Husband
## 3018 Married-civ-spouse Prof-specialty Husband
## 3019 Never-married Farming-fishing Own-child
## 3020 Never-married Exec-managerial Not-in-family
## 3021 Married-civ-spouse Prof-specialty Husband
## 3022 Married-civ-spouse Craft-repair Husband
## 3023 Married-civ-spouse Craft-repair Husband
## 3024 Never-married Craft-repair Not-in-family
## 3025 Never-married Sales Own-child
## 3026 Married-civ-spouse Craft-repair Husband
## 3027 Married-civ-spouse Machine-op-inspct Husband
## 3028 Married-civ-spouse Prof-specialty Husband
## 3029 Never-married Other-service Not-in-family
## 3030 Married-civ-spouse Exec-managerial Husband
## 3031 Widowed Machine-op-inspct Unmarried
## 3032 Separated Machine-op-inspct Unmarried
## 3033 Married-civ-spouse Craft-repair Husband
## 3034 Divorced ? Not-in-family
## 3035 Married-AF-spouse Adm-clerical Husband
## 3036 Married-civ-spouse Sales Husband
## 3037 Widowed Prof-specialty Unmarried
## 3038 Divorced Adm-clerical Own-child
## 3039 Married-civ-spouse Craft-repair Husband
## 3040 Married-civ-spouse Sales Husband
## 3041 Never-married Exec-managerial Not-in-family
## 3042 Never-married Prof-specialty Not-in-family
## 3043 Married-civ-spouse ? Husband
## 3044 Never-married Adm-clerical Other-relative
## 3045 Never-married Handlers-cleaners Other-relative
## 3046 Divorced Transport-moving Not-in-family
## 3047 Divorced Adm-clerical Not-in-family
## 3048 Never-married Sales Not-in-family
## 3049 Married-civ-spouse Farming-fishing Husband
## 3050 Married-civ-spouse Exec-managerial Husband
## 3051 Married-civ-spouse Transport-moving Husband
## 3052 Divorced Sales Unmarried
## 3053 Married-civ-spouse Transport-moving Husband
## 3054 Married-civ-spouse Handlers-cleaners Husband
## 3055 Married-civ-spouse Exec-managerial Husband
## 3056 Married-civ-spouse Machine-op-inspct Husband
## 3057 Married-civ-spouse Sales Husband
## 3058 Married-civ-spouse Exec-managerial Husband
## 3059 Never-married Adm-clerical Own-child
## 3060 Married-civ-spouse Transport-moving Husband
## 3061 Never-married Other-service Own-child
## 3062 Divorced Prof-specialty Not-in-family
## 3063 Married-civ-spouse Craft-repair Husband
## 3064 Divorced Handlers-cleaners Not-in-family
## 3065 Married-spouse-absent Prof-specialty Not-in-family
## 3066 Widowed ? Not-in-family
## 3067 Never-married Transport-moving Own-child
## 3068 Widowed Adm-clerical Not-in-family
## 3069 Divorced Exec-managerial Not-in-family
## 3070 Divorced Other-service Unmarried
## 3071 Never-married Exec-managerial Own-child
## 3072 Married-civ-spouse Exec-managerial Husband
## 3073 Never-married ? Own-child
## 3074 Never-married Craft-repair Own-child
## 3075 Never-married Sales Own-child
## 3076 Never-married Sales Own-child
## 3077 Married-civ-spouse Sales Husband
## 3078 Married-civ-spouse Exec-managerial Husband
## 3079 Never-married Other-service Own-child
## 3080 Married-civ-spouse Exec-managerial Husband
## 3081 Married-civ-spouse Prof-specialty Husband
## 3082 Divorced Machine-op-inspct Unmarried
## 3083 Widowed Exec-managerial Unmarried
## 3084 Never-married Craft-repair Own-child
## 3085 Never-married Handlers-cleaners Own-child
## 3086 Never-married Farming-fishing Own-child
## 3087 Divorced Machine-op-inspct Unmarried
## 3088 Divorced Adm-clerical Unmarried
## 3089 Never-married ? Own-child
## 3090 Married-civ-spouse Sales Husband
## 3091 Separated Adm-clerical Not-in-family
## 3092 Married-civ-spouse Tech-support Husband
## 3093 Married-civ-spouse Farming-fishing Husband
## 3094 Married-civ-spouse Exec-managerial Husband
## 3095 Married-civ-spouse Prof-specialty Wife
## 3096 Married-civ-spouse Exec-managerial Husband
## 3097 Never-married ? Unmarried
## 3098 Never-married Prof-specialty Not-in-family
## 3099 Divorced Prof-specialty Unmarried
## 3100 Never-married Sales Own-child
## 3101 Married-civ-spouse Machine-op-inspct Husband
## 3102 Married-civ-spouse Farming-fishing Husband
## 3103 Divorced Machine-op-inspct Own-child
## 3104 Never-married Other-service Own-child
## 3105 Married-civ-spouse Sales Husband
## 3106 Married-civ-spouse Prof-specialty Husband
## 3107 Married-civ-spouse Exec-managerial Husband
## 3108 Married-civ-spouse Prof-specialty Husband
## 3109 Married-civ-spouse Exec-managerial Own-child
## 3110 Never-married Sales Own-child
## 3111 Never-married Prof-specialty Not-in-family
## 3112 Married-civ-spouse Prof-specialty Husband
## 3113 Never-married Adm-clerical Own-child
## 3114 Married-civ-spouse Exec-managerial Husband
## 3115 Never-married Other-service Own-child
## 3116 Married-civ-spouse Tech-support Husband
## 3117 Married-civ-spouse Sales Husband
## 3118 Never-married Farming-fishing Not-in-family
## 3119 Never-married Farming-fishing Not-in-family
## 3120 Married-civ-spouse ? Husband
## 3121 Divorced Adm-clerical Unmarried
## 3122 Never-married Craft-repair Own-child
## 3123 Married-civ-spouse Adm-clerical Wife
## 3124 Divorced Adm-clerical Unmarried
## 3125 Never-married Adm-clerical Own-child
## 3126 Married-civ-spouse Exec-managerial Husband
## 3127 Married-civ-spouse Adm-clerical Husband
## 3128 Never-married Exec-managerial Own-child
## 3129 Married-civ-spouse Machine-op-inspct Husband
## 3130 Separated Prof-specialty Not-in-family
## 3131 Divorced Other-service Unmarried
## 3132 Never-married ? Own-child
## 3133 Married-civ-spouse Transport-moving Husband
## 3134 Never-married Craft-repair Own-child
## 3135 Married-civ-spouse Prof-specialty Husband
## 3136 Married-civ-spouse Transport-moving Husband
## 3137 Never-married Farming-fishing Not-in-family
## 3138 Married-civ-spouse Sales Husband
## 3139 Divorced Adm-clerical Unmarried
## 3140 Never-married Adm-clerical Own-child
## 3141 Married-civ-spouse Craft-repair Husband
## 3142 Married-civ-spouse Craft-repair Husband
## 3143 Married-civ-spouse Craft-repair Husband
## 3144 Married-civ-spouse Exec-managerial Husband
## 3145 Married-civ-spouse Exec-managerial Husband
## 3146 Married-civ-spouse Sales Husband
## 3147 Divorced ? Not-in-family
## 3148 Never-married Prof-specialty Not-in-family
## 3149 Married-civ-spouse Exec-managerial Husband
## 3150 Married-civ-spouse Exec-managerial Husband
## 3151 Married-civ-spouse Handlers-cleaners Husband
## 3152 Never-married Prof-specialty Not-in-family
## 3153 Married-civ-spouse Exec-managerial Husband
## 3154 Married-civ-spouse Prof-specialty Husband
## 3155 Married-civ-spouse Sales Husband
## 3156 Married-civ-spouse Sales Husband
## 3157 Never-married Prof-specialty Own-child
## 3158 Married-civ-spouse Other-service Husband
## 3159 Widowed Craft-repair Not-in-family
## 3160 Married-civ-spouse Exec-managerial Husband
## 3161 Divorced Prof-specialty Unmarried
## 3162 Never-married Craft-repair Own-child
## 3163 Never-married Machine-op-inspct Not-in-family
## 3164 Married-civ-spouse Exec-managerial Wife
## 3165 Married-civ-spouse Adm-clerical Husband
## 3166 Married-civ-spouse Exec-managerial Wife
## 3167 Married-civ-spouse Craft-repair Husband
## 3168 Married-civ-spouse Prof-specialty Husband
## 3169 Married-civ-spouse Exec-managerial Husband
## 3170 Married-civ-spouse Transport-moving Husband
## 3171 Never-married Adm-clerical Own-child
## 3172 Married-civ-spouse Sales Husband
## 3173 Never-married Prof-specialty Not-in-family
## 3174 Married-civ-spouse Handlers-cleaners Wife
## 3175 Married-civ-spouse Exec-managerial Husband
## 3176 Divorced Prof-specialty Not-in-family
## 3177 Married-civ-spouse Adm-clerical Wife
## 3178 Never-married Handlers-cleaners Own-child
## 3179 Married-spouse-absent Other-service Unmarried
## 3180 Never-married Other-service Not-in-family
## 3181 Married-civ-spouse Protective-serv Husband
## 3182 Never-married Craft-repair Own-child
## 3183 Never-married Machine-op-inspct Unmarried
## 3184 Widowed Sales Not-in-family
## 3185 Never-married Prof-specialty Unmarried
## 3186 Married-civ-spouse Sales Husband
## 3187 Married-civ-spouse Sales Husband
## 3188 Married-spouse-absent Other-service Not-in-family
## 3189 Separated Sales Unmarried
## 3190 Married-civ-spouse Machine-op-inspct Husband
## 3191 Married-civ-spouse Craft-repair Husband
## 3192 Separated Machine-op-inspct Not-in-family
## 3193 Never-married Other-service Not-in-family
## 3194 Divorced Machine-op-inspct Unmarried
## 3195 Divorced Other-service Own-child
## 3196 Married-spouse-absent Sales Unmarried
## 3197 Married-spouse-absent Farming-fishing Other-relative
## 3198 Married-civ-spouse Sales Husband
## 3199 Married-civ-spouse Machine-op-inspct Wife
## 3200 Married-civ-spouse Sales Husband
## 3201 Never-married Exec-managerial Own-child
## 3202 Never-married Machine-op-inspct Not-in-family
## 3203 Never-married Sales Own-child
## 3204 Married-civ-spouse Sales Wife
## 3205 Widowed Sales Unmarried
## 3206 Never-married Craft-repair Unmarried
## 3207 Married-civ-spouse Exec-managerial Husband
## 3208 Never-married ? Own-child
## 3209 Widowed Machine-op-inspct Not-in-family
## 3210 Widowed Prof-specialty Not-in-family
## 3211 Married-civ-spouse Handlers-cleaners Husband
## 3212 Widowed ? Not-in-family
## 3213 Married-civ-spouse Sales Husband
## 3214 Widowed Other-service Not-in-family
## 3215 Never-married Adm-clerical Own-child
## 3216 Married-civ-spouse Craft-repair Husband
## 3217 Married-civ-spouse Farming-fishing Husband
## 3218 Married-civ-spouse Craft-repair Husband
## 3219 Married-civ-spouse Sales Husband
## 3220 Divorced Adm-clerical Unmarried
## 3221 Divorced Exec-managerial Not-in-family
## 3222 Married-civ-spouse Other-service Husband
## 3223 Never-married Craft-repair Unmarried
## 3224 Divorced Adm-clerical Not-in-family
## 3225 Married-civ-spouse Sales Wife
## 3226 Married-civ-spouse Farming-fishing Husband
## 3227 Married-civ-spouse Transport-moving Husband
## 3228 Married-civ-spouse Sales Husband
## 3229 Never-married ? Own-child
## 3230 Divorced Machine-op-inspct Not-in-family
## 3231 Married-civ-spouse Exec-managerial Wife
## 3232 Married-civ-spouse ? Husband
## 3233 Separated Exec-managerial Not-in-family
## 3234 Divorced Machine-op-inspct Not-in-family
## 3235 Never-married Other-service Unmarried
## 3236 Married-civ-spouse Craft-repair Husband
## 3237 Married-spouse-absent Craft-repair Not-in-family
## 3238 Never-married Prof-specialty Own-child
## 3239 Married-civ-spouse Exec-managerial Husband
## 3240 Never-married ? Own-child
## 3241 Never-married Other-service Unmarried
## 3242 Separated Other-service Not-in-family
## 3243 Never-married Handlers-cleaners Not-in-family
## 3244 Never-married Machine-op-inspct Other-relative
## 3245 Separated Prof-specialty Unmarried
## 3246 Married-civ-spouse Sales Husband
## 3247 Never-married Sales Not-in-family
## 3248 Married-civ-spouse Adm-clerical Wife
## 3249 Married-civ-spouse Sales Husband
## 3250 Married-civ-spouse Exec-managerial Husband
## 3251 Married-civ-spouse Adm-clerical Husband
## 3252 Never-married Sales Own-child
## 3253 Never-married Prof-specialty Not-in-family
## 3254 Never-married Handlers-cleaners Unmarried
## 3255 Married-civ-spouse Craft-repair Husband
## 3256 Divorced ? Not-in-family
## 3257 Married-civ-spouse Prof-specialty Husband
## 3258 Divorced Craft-repair Own-child
## 3259 Never-married Handlers-cleaners Own-child
## 3260 Divorced Prof-specialty Unmarried
## 3261 Married-civ-spouse Prof-specialty Husband
## 3262 Never-married Craft-repair Not-in-family
## 3263 Married-civ-spouse Transport-moving Husband
## 3264 Never-married Adm-clerical Not-in-family
## 3265 Separated Exec-managerial Not-in-family
## 3266 Married-civ-spouse Exec-managerial Husband
## 3267 Married-civ-spouse Tech-support Husband
## 3268 Married-civ-spouse Prof-specialty Husband
## 3269 Never-married Prof-specialty Not-in-family
## 3270 Married-civ-spouse ? Wife
## 3271 Widowed Other-service Not-in-family
## 3272 Married-civ-spouse Exec-managerial Husband
## 3273 Never-married Craft-repair Not-in-family
## 3274 Married-civ-spouse Craft-repair Husband
## 3275 Married-civ-spouse Prof-specialty Husband
## 3276 Divorced Adm-clerical Unmarried
## 3277 Never-married Prof-specialty Not-in-family
## 3278 Married-civ-spouse Craft-repair Husband
## 3279 Married-civ-spouse Craft-repair Husband
## 3280 Divorced Exec-managerial Unmarried
## 3281 Never-married Transport-moving Not-in-family
## 3282 Never-married Prof-specialty Not-in-family
## 3283 Never-married Farming-fishing Other-relative
## 3284 Separated Other-service Other-relative
## 3285 Married-civ-spouse Adm-clerical Husband
## 3286 Never-married Other-service Own-child
## 3287 Never-married Other-service Not-in-family
## 3288 Married-civ-spouse Farming-fishing Husband
## 3289 Married-civ-spouse Craft-repair Husband
## 3290 Married-civ-spouse Craft-repair Husband
## 3291 Divorced Exec-managerial Unmarried
## 3292 Married-civ-spouse ? Husband
## 3293 Married-civ-spouse Prof-specialty Husband
## 3294 Married-civ-spouse Exec-managerial Husband
## 3295 Divorced Exec-managerial Unmarried
## 3296 Never-married Tech-support Not-in-family
## 3297 Married-civ-spouse Transport-moving Husband
## 3298 Married-civ-spouse ? Husband
## 3299 Never-married Other-service Unmarried
## 3300 Never-married Sales Not-in-family
## 3301 Divorced Adm-clerical Unmarried
## 3302 Divorced Craft-repair Other-relative
## 3303 Married-civ-spouse Adm-clerical Husband
## 3304 Married-civ-spouse Other-service Husband
## 3305 Divorced Adm-clerical Unmarried
## 3306 Divorced Exec-managerial Not-in-family
## 3307 Never-married Sales Not-in-family
## 3308 Divorced Adm-clerical Not-in-family
## 3309 Married-civ-spouse Handlers-cleaners Husband
## 3310 Married-civ-spouse Adm-clerical Husband
## 3311 Never-married Sales Not-in-family
## 3312 Married-civ-spouse Prof-specialty Husband
## 3313 Married-civ-spouse Farming-fishing Husband
## 3314 Never-married Prof-specialty Own-child
## 3315 Never-married Transport-moving Not-in-family
## 3316 Widowed Sales Not-in-family
## 3317 Never-married Protective-serv Not-in-family
## 3318 Married-civ-spouse Tech-support Husband
## 3319 Married-civ-spouse Exec-managerial Husband
## 3320 Never-married Machine-op-inspct Own-child
## 3321 Never-married Adm-clerical Own-child
## 3322 Married-civ-spouse Tech-support Husband
## 3323 Married-civ-spouse Craft-repair Husband
## 3324 Married-civ-spouse Transport-moving Husband
## 3325 Widowed Prof-specialty Not-in-family
## 3326 Divorced Adm-clerical Unmarried
## 3327 Divorced Farming-fishing Not-in-family
## 3328 Married-civ-spouse Prof-specialty Husband
## 3329 Married-civ-spouse Prof-specialty Husband
## 3330 Divorced Adm-clerical Unmarried
## 3331 Never-married ? Not-in-family
## 3332 Never-married Tech-support Other-relative
## 3333 Divorced Adm-clerical Unmarried
## 3334 Married-civ-spouse Craft-repair Husband
## 3335 Never-married Handlers-cleaners Own-child
## 3336 Divorced Machine-op-inspct Not-in-family
## 3337 Married-civ-spouse Adm-clerical Wife
## 3338 Married-civ-spouse Exec-managerial Husband
## 3339 Widowed ? Not-in-family
## 3340 Widowed Craft-repair Unmarried
## 3341 Separated Other-service Not-in-family
## 3342 Divorced Adm-clerical Unmarried
## 3343 Separated Other-service Unmarried
## 3344 Married-civ-spouse Sales Husband
## 3345 Never-married Adm-clerical Own-child
## 3346 Never-married Machine-op-inspct Not-in-family
## 3347 Separated Adm-clerical Unmarried
## 3348 Married-spouse-absent Craft-repair Other-relative
## 3349 Married-civ-spouse Craft-repair Husband
## 3350 Married-civ-spouse Prof-specialty Wife
## 3351 Married-spouse-absent Farming-fishing Not-in-family
## 3352 Never-married ? Not-in-family
## 3353 Married-civ-spouse Adm-clerical Wife
## 3354 Married-civ-spouse Craft-repair Husband
## 3355 Never-married Handlers-cleaners Unmarried
## 3356 Never-married Prof-specialty Not-in-family
## 3357 Never-married Adm-clerical Own-child
## 3358 Never-married Adm-clerical Own-child
## 3359 Married-civ-spouse Adm-clerical Husband
## 3360 Never-married Exec-managerial Not-in-family
## 3361 Married-civ-spouse Sales Wife
## 3362 Never-married Priv-house-serv Other-relative
## 3363 Never-married Other-service Own-child
## 3364 Married-civ-spouse Exec-managerial Husband
## 3365 Never-married Adm-clerical Own-child
## 3366 Married-civ-spouse Exec-managerial Husband
## 3367 Married-civ-spouse Machine-op-inspct Husband
## 3368 Married-civ-spouse Exec-managerial Husband
## 3369 Married-civ-spouse Prof-specialty Husband
## 3370 Married-civ-spouse Exec-managerial Husband
## 3371 Married-civ-spouse Prof-specialty Husband
## 3372 Never-married ? Own-child
## 3373 Separated Other-service Own-child
## 3374 Divorced Tech-support Unmarried
## 3375 Married-civ-spouse Craft-repair Husband
## 3376 Married-civ-spouse Craft-repair Husband
## 3377 Married-civ-spouse Transport-moving Husband
## 3378 Married-civ-spouse Sales Husband
## 3379 Married-civ-spouse Craft-repair Husband
## 3380 Separated Adm-clerical Unmarried
## 3381 Never-married Machine-op-inspct Unmarried
## 3382 Married-civ-spouse Exec-managerial Husband
## 3383 Married-civ-spouse Craft-repair Husband
## 3384 Married-civ-spouse Craft-repair Husband
## 3385 Married-civ-spouse Machine-op-inspct Wife
## 3386 Married-civ-spouse Transport-moving Husband
## 3387 Married-civ-spouse Sales Husband
## 3388 Married-spouse-absent ? Not-in-family
## 3389 Married-civ-spouse Farming-fishing Husband
## 3390 Married-civ-spouse Prof-specialty Husband
## 3391 Widowed Prof-specialty Unmarried
## 3392 Never-married Other-service Own-child
## 3393 Married-civ-spouse Prof-specialty Husband
## 3394 Married-civ-spouse Farming-fishing Husband
## 3395 Never-married Other-service Own-child
## 3396 Separated Prof-specialty Not-in-family
## 3397 Never-married Exec-managerial Not-in-family
## 3398 Never-married Machine-op-inspct Own-child
## 3399 Married-civ-spouse Exec-managerial Husband
## 3400 Married-civ-spouse Transport-moving Husband
## 3401 Married-civ-spouse Craft-repair Husband
## 3402 Married-civ-spouse Craft-repair Husband
## 3403 Married-civ-spouse ? Husband
## 3404 Never-married Farming-fishing Not-in-family
## 3405 Married-civ-spouse Adm-clerical Husband
## 3406 Married-civ-spouse Exec-managerial Husband
## 3407 Married-civ-spouse Machine-op-inspct Husband
## 3408 Married-civ-spouse Exec-managerial Husband
## 3409 Married-civ-spouse Sales Husband
## 3410 Separated Handlers-cleaners Unmarried
## 3411 Married-civ-spouse Exec-managerial Husband
## 3412 Never-married Machine-op-inspct Not-in-family
## 3413 Married-civ-spouse Machine-op-inspct Husband
## 3414 Married-civ-spouse Sales Husband
## 3415 Married-civ-spouse Craft-repair Husband
## 3416 Married-civ-spouse Craft-repair Husband
## 3417 Separated Exec-managerial Unmarried
## 3418 Never-married Sales Not-in-family
## 3419 Married-civ-spouse Exec-managerial Husband
## 3420 Never-married Exec-managerial Not-in-family
## 3421 Never-married Other-service Not-in-family
## 3422 Separated Adm-clerical Not-in-family
## 3423 Divorced Other-service Not-in-family
## 3424 Married-spouse-absent Prof-specialty Not-in-family
## 3425 Married-civ-spouse Farming-fishing Husband
## 3426 Never-married Other-service Own-child
## 3427 Divorced Prof-specialty Not-in-family
## 3428 Married-civ-spouse Sales Husband
## 3429 Married-civ-spouse Sales Husband
## 3430 Never-married Transport-moving Own-child
## 3431 Married-civ-spouse Tech-support Wife
## 3432 Never-married Sales Own-child
## 3433 Married-civ-spouse Machine-op-inspct Husband
## 3434 Married-civ-spouse Sales Husband
## 3435 Divorced Other-service Not-in-family
## 3436 Married-civ-spouse Transport-moving Husband
## 3437 Married-civ-spouse Craft-repair Husband
## 3438 Married-civ-spouse Craft-repair Husband
## 3439 Married-civ-spouse Craft-repair Husband
## 3440 Divorced ? Not-in-family
## 3441 Never-married Prof-specialty Not-in-family
## 3442 Never-married Handlers-cleaners Not-in-family
## 3443 Married-civ-spouse Craft-repair Husband
## 3444 Never-married Other-service Other-relative
## 3445 Divorced Prof-specialty Not-in-family
## 3446 Married-civ-spouse Exec-managerial Husband
## 3447 Married-civ-spouse Farming-fishing Husband
## 3448 Married-civ-spouse Craft-repair Husband
## 3449 Married-civ-spouse Sales Husband
## 3450 Never-married Exec-managerial Not-in-family
## 3451 Divorced Sales Own-child
## 3452 Married-civ-spouse Prof-specialty Wife
## 3453 Never-married Adm-clerical Own-child
## 3454 Never-married ? Other-relative
## 3455 Divorced Prof-specialty Unmarried
## 3456 Never-married Machine-op-inspct Own-child
## 3457 Never-married ? Own-child
## 3458 Never-married Prof-specialty Own-child
## 3459 Married-civ-spouse Sales Husband
## 3460 Never-married ? Unmarried
## 3461 Married-civ-spouse Craft-repair Husband
## 3462 Widowed Other-service Unmarried
## 3463 Divorced Transport-moving Not-in-family
## 3464 Never-married Prof-specialty Own-child
## 3465 Married-civ-spouse Adm-clerical Husband
## 3466 Never-married Prof-specialty Not-in-family
## 3467 Married-civ-spouse Exec-managerial Husband
## 3468 Married-civ-spouse Machine-op-inspct Husband
## 3469 Married-civ-spouse Prof-specialty Wife
## 3470 Married-civ-spouse Sales Husband
## 3471 Married-civ-spouse Exec-managerial Husband
## 3472 Married-civ-spouse Transport-moving Husband
## 3473 Never-married Prof-specialty Own-child
## 3474 Never-married Other-service Own-child
## 3475 Divorced Prof-specialty Unmarried
## 3476 Married-civ-spouse Exec-managerial Husband
## 3477 Never-married Sales Own-child
## 3478 Married-civ-spouse Machine-op-inspct Husband
## 3479 Never-married Sales Unmarried
## 3480 Never-married Exec-managerial Not-in-family
## 3481 Married-civ-spouse Tech-support Husband
## 3482 Never-married Handlers-cleaners Not-in-family
## 3483 Divorced Sales Unmarried
## 3484 Married-civ-spouse Prof-specialty Husband
## 3485 Never-married Craft-repair Own-child
## 3486 Married-civ-spouse Exec-managerial Husband
## 3487 Never-married ? Not-in-family
## 3488 Never-married Machine-op-inspct Not-in-family
## 3489 Never-married Exec-managerial Not-in-family
## 3490 Married-civ-spouse Prof-specialty Wife
## 3491 Married-civ-spouse Other-service Husband
## 3492 Never-married Adm-clerical Unmarried
## 3493 Married-civ-spouse Prof-specialty Husband
## 3494 Married-civ-spouse Other-service Wife
## 3495 Never-married Exec-managerial Not-in-family
## 3496 Never-married Other-service Own-child
## 3497 Never-married Other-service Other-relative
## 3498 Married-civ-spouse Craft-repair Husband
## 3499 Married-civ-spouse Handlers-cleaners Husband
## 3500 Married-civ-spouse Prof-specialty Husband
## 3501 Married-civ-spouse Tech-support Husband
## 3502 Never-married Prof-specialty Unmarried
## 3503 Married-civ-spouse Craft-repair Husband
## 3504 Divorced Exec-managerial Unmarried
## 3505 Never-married Exec-managerial Not-in-family
## 3506 Married-civ-spouse Exec-managerial Husband
## 3507 Divorced Adm-clerical Own-child
## 3508 Never-married Protective-serv Not-in-family
## 3509 Married-civ-spouse Transport-moving Husband
## 3510 Never-married Sales Own-child
## 3511 Widowed Sales Not-in-family
## 3512 Divorced Craft-repair Unmarried
## 3513 Never-married Adm-clerical Own-child
## 3514 Never-married Tech-support Not-in-family
## 3515 Divorced Exec-managerial Unmarried
## 3516 Divorced Prof-specialty Not-in-family
## 3517 Never-married ? Own-child
## 3518 Married-civ-spouse Craft-repair Husband
## 3519 Married-civ-spouse Protective-serv Husband
## 3520 Divorced Other-service Unmarried
## 3521 Married-civ-spouse Craft-repair Husband
## 3522 Married-civ-spouse Craft-repair Husband
## 3523 Married-civ-spouse Craft-repair Husband
## 3524 Married-civ-spouse Exec-managerial Husband
## 3525 Never-married Handlers-cleaners Own-child
## 3526 Married-civ-spouse Machine-op-inspct Husband
## 3527 Never-married Adm-clerical Own-child
## 3528 Married-civ-spouse Handlers-cleaners Husband
## 3529 Never-married Other-service Own-child
## 3530 Divorced Machine-op-inspct Not-in-family
## 3531 Married-civ-spouse Sales Husband
## 3532 Divorced ? Unmarried
## 3533 Never-married Farming-fishing Not-in-family
## 3534 Married-civ-spouse Transport-moving Husband
## 3535 Never-married Other-service Own-child
## 3536 Never-married Other-service Other-relative
## 3537 Married-civ-spouse Exec-managerial Husband
## 3538 Widowed Adm-clerical Not-in-family
## 3539 Married-civ-spouse Exec-managerial Husband
## 3540 Married-civ-spouse Craft-repair Husband
## 3541 Married-civ-spouse Sales Husband
## 3542 Married-civ-spouse Farming-fishing Husband
## 3543 Never-married Sales Not-in-family
## 3544 Married-civ-spouse Adm-clerical Wife
## 3545 Divorced Adm-clerical Unmarried
## 3546 Separated Sales Not-in-family
## 3547 Never-married Handlers-cleaners Own-child
## 3548 Married-civ-spouse Craft-repair Husband
## 3549 Never-married Sales Unmarried
## 3550 Never-married Sales Own-child
## 3551 Married-civ-spouse Exec-managerial Husband
## 3552 Never-married Sales Not-in-family
## 3553 Married-civ-spouse Craft-repair Husband
## 3554 Divorced Sales Not-in-family
## 3555 Never-married Sales Unmarried
## 3556 Never-married ? Not-in-family
## 3557 Divorced Exec-managerial Not-in-family
## 3558 Never-married Adm-clerical Not-in-family
## 3559 Never-married Prof-specialty Own-child
## 3560 Divorced Exec-managerial Not-in-family
## 3561 Divorced Other-service Not-in-family
## 3562 Married-civ-spouse Farming-fishing Husband
## 3563 Never-married Machine-op-inspct Not-in-family
## 3564 Divorced Craft-repair Not-in-family
## 3565 Married-civ-spouse Exec-managerial Husband
## 3566 Divorced Other-service Unmarried
## 3567 Never-married Prof-specialty Not-in-family
## 3568 Married-civ-spouse Machine-op-inspct Husband
## 3569 Married-civ-spouse Machine-op-inspct Husband
## 3570 Never-married Tech-support Not-in-family
## 3571 Married-civ-spouse Machine-op-inspct Husband
## 3572 Married-civ-spouse Craft-repair Husband
## 3573 Never-married ? Own-child
## 3574 Never-married ? Not-in-family
## 3575 Married-civ-spouse Prof-specialty Husband
## 3576 Married-civ-spouse Exec-managerial Husband
## 3577 Never-married Other-service Own-child
## 3578 Married-civ-spouse Prof-specialty Husband
## 3579 Married-civ-spouse Prof-specialty Husband
## 3580 Never-married ? Not-in-family
## 3581 Married-civ-spouse Craft-repair Husband
## 3582 Married-spouse-absent Transport-moving Unmarried
## 3583 Married-civ-spouse Other-service Husband
## 3584 Married-civ-spouse Protective-serv Husband
## 3585 Never-married Prof-specialty Not-in-family
## 3586 Never-married Other-service Other-relative
## 3587 Never-married Prof-specialty Not-in-family
## 3588 Divorced Sales Unmarried
## 3589 Widowed ? Not-in-family
## 3590 Never-married Exec-managerial Own-child
## 3591 Never-married Tech-support Not-in-family
## 3592 Never-married Prof-specialty Own-child
## 3593 Widowed ? Not-in-family
## 3594 Married-civ-spouse Farming-fishing Wife
## 3595 Married-civ-spouse ? Wife
## 3596 Married-civ-spouse Other-service Wife
## 3597 Never-married Adm-clerical Own-child
## 3598 Never-married ? Own-child
## 3599 Divorced Adm-clerical Not-in-family
## 3600 Married-civ-spouse Tech-support Husband
## 3601 Divorced Other-service Not-in-family
## 3602 Never-married Prof-specialty Not-in-family
## 3603 Married-civ-spouse Prof-specialty Husband
## 3604 Married-civ-spouse Transport-moving Husband
## 3605 Married-civ-spouse Sales Husband
## 3606 Never-married Other-service Own-child
## 3607 Married-civ-spouse Transport-moving Husband
## 3608 Separated Craft-repair Not-in-family
## 3609 Never-married Machine-op-inspct Own-child
## 3610 Never-married Prof-specialty Not-in-family
## 3611 Married-civ-spouse Adm-clerical Husband
## 3612 Married-civ-spouse Sales Wife
## 3613 Married-civ-spouse Tech-support Husband
## 3614 Never-married Adm-clerical Own-child
## 3615 Married-civ-spouse Craft-repair Husband
## 3616 Divorced Tech-support Own-child
## 3617 Never-married Exec-managerial Not-in-family
## 3618 Divorced Farming-fishing Unmarried
## 3619 Never-married Other-service Own-child
## 3620 Married-civ-spouse Sales Husband
## 3621 Separated Adm-clerical Other-relative
## 3622 Never-married Sales Not-in-family
## 3623 Never-married Priv-house-serv Not-in-family
## 3624 Married-civ-spouse Machine-op-inspct Husband
## 3625 Widowed Adm-clerical Not-in-family
## 3626 Married-civ-spouse Exec-managerial Husband
## 3627 Married-civ-spouse Adm-clerical Wife
## 3628 Married-civ-spouse Prof-specialty Husband
## 3629 Never-married Exec-managerial Other-relative
## 3630 Married-civ-spouse Prof-specialty Husband
## 3631 Divorced Prof-specialty Unmarried
## 3632 Married-civ-spouse ? Husband
## 3633 Never-married Adm-clerical Own-child
## 3634 Widowed Protective-serv Not-in-family
## 3635 Divorced Craft-repair Not-in-family
## 3636 Married-civ-spouse Machine-op-inspct Husband
## 3637 Married-civ-spouse Machine-op-inspct Husband
## 3638 Divorced Adm-clerical Unmarried
## 3639 Never-married Craft-repair Not-in-family
## 3640 Married-civ-spouse Sales Husband
## 3641 Never-married Transport-moving Not-in-family
## 3642 Never-married Transport-moving Unmarried
## 3643 Married-civ-spouse Machine-op-inspct Husband
## 3644 Married-civ-spouse Craft-repair Husband
## 3645 Married-civ-spouse Machine-op-inspct Husband
## 3646 Never-married Other-service Own-child
## 3647 Married-civ-spouse Sales Wife
## 3648 Married-civ-spouse Machine-op-inspct Wife
## 3649 Never-married Exec-managerial Not-in-family
## 3650 Never-married Other-service Own-child
## 3651 Never-married Adm-clerical Own-child
## 3652 Married-civ-spouse Exec-managerial Husband
## 3653 Married-civ-spouse Prof-specialty Husband
## 3654 Divorced Craft-repair Own-child
## 3655 Married-civ-spouse Transport-moving Husband
## 3656 Married-civ-spouse Prof-specialty Husband
## 3657 Married-civ-spouse Machine-op-inspct Husband
## 3658 Married-civ-spouse Other-service Husband
## 3659 Never-married Sales Own-child
## 3660 Married-civ-spouse Handlers-cleaners Husband
## 3661 Married-civ-spouse Transport-moving Husband
## 3662 Never-married Adm-clerical Not-in-family
## 3663 Never-married Farming-fishing Not-in-family
## 3664 Married-civ-spouse Machine-op-inspct Husband
## 3665 Married-civ-spouse Prof-specialty Husband
## 3666 Never-married Sales Not-in-family
## 3667 Married-civ-spouse Exec-managerial Husband
## 3668 Divorced Exec-managerial Unmarried
## 3669 Married-civ-spouse Transport-moving Husband
## 3670 Married-civ-spouse Machine-op-inspct Husband
## 3671 Divorced ? Own-child
## 3672 Married-civ-spouse Exec-managerial Husband
## 3673 Married-civ-spouse Prof-specialty Husband
## 3674 Married-civ-spouse Craft-repair Husband
## 3675 Married-civ-spouse Prof-specialty Husband
## 3676 Never-married Handlers-cleaners Other-relative
## 3677 Married-civ-spouse Machine-op-inspct Husband
## 3678 Divorced Prof-specialty Not-in-family
## 3679 Married-civ-spouse Adm-clerical Husband
## 3680 Never-married Sales Own-child
## 3681 Married-civ-spouse Protective-serv Husband
## 3682 Never-married Sales Own-child
## 3683 Married-civ-spouse Exec-managerial Husband
## 3684 Divorced Adm-clerical Not-in-family
## 3685 Married-civ-spouse Exec-managerial Husband
## 3686 Married-civ-spouse Craft-repair Husband
## 3687 Never-married Other-service Other-relative
## 3688 Never-married Handlers-cleaners Other-relative
## 3689 Divorced Handlers-cleaners Not-in-family
## 3690 Married-civ-spouse Exec-managerial Husband
## 3691 Married-civ-spouse Transport-moving Husband
## 3692 Married-civ-spouse Craft-repair Husband
## 3693 Married-civ-spouse Craft-repair Husband
## 3694 Married-civ-spouse Adm-clerical Wife
## 3695 Married-civ-spouse Other-service Husband
## 3696 Divorced Sales Unmarried
## 3697 Never-married Machine-op-inspct Other-relative
## 3698 Married-civ-spouse Sales Husband
## 3699 Married-civ-spouse Exec-managerial Husband
## 3700 Divorced Machine-op-inspct Not-in-family
## 3701 Married-civ-spouse Exec-managerial Husband
## 3702 Married-civ-spouse Handlers-cleaners Husband
## 3703 Never-married Craft-repair Not-in-family
## 3704 Married-civ-spouse ? Husband
## 3705 Divorced Prof-specialty Not-in-family
## 3706 Married-civ-spouse Craft-repair Husband
## 3707 Never-married Other-service Other-relative
## 3708 Married-civ-spouse Craft-repair Husband
## 3709 Married-civ-spouse Adm-clerical Husband
## 3710 Married-civ-spouse Prof-specialty Wife
## 3711 Never-married Adm-clerical Not-in-family
## 3712 Married-civ-spouse Farming-fishing Husband
## 3713 Never-married Adm-clerical Own-child
## 3714 Divorced Protective-serv Own-child
## 3715 Married-civ-spouse Exec-managerial Other-relative
## 3716 Never-married Adm-clerical Own-child
## 3717 Married-civ-spouse Exec-managerial Husband
## 3718 Never-married Exec-managerial Own-child
## 3719 Never-married Other-service Own-child
## 3720 Never-married Adm-clerical Own-child
## 3721 Married-civ-spouse Transport-moving Husband
## 3722 Married-civ-spouse Exec-managerial Husband
## 3723 Never-married Adm-clerical Unmarried
## 3724 Married-civ-spouse Sales Husband
## 3725 Married-civ-spouse Exec-managerial Husband
## 3726 Never-married ? Own-child
## 3727 Married-civ-spouse Adm-clerical Wife
## 3728 Married-civ-spouse Prof-specialty Wife
## 3729 Married-civ-spouse Craft-repair Husband
## 3730 Never-married Exec-managerial Unmarried
## 3731 Never-married Adm-clerical Not-in-family
## 3732 Married-civ-spouse Craft-repair Husband
## 3733 Married-civ-spouse Machine-op-inspct Husband
## 3734 Married-civ-spouse Prof-specialty Wife
## 3735 Never-married Sales Own-child
## 3736 Married-civ-spouse Adm-clerical Husband
## 3737 Married-civ-spouse ? Wife
## 3738 Never-married Other-service Other-relative
## 3739 Married-civ-spouse Craft-repair Husband
## 3740 Married-civ-spouse Other-service Husband
## 3741 Married-civ-spouse Other-service Husband
## 3742 Married-civ-spouse Adm-clerical Husband
## 3743 Divorced Transport-moving Not-in-family
## 3744 Divorced Prof-specialty Not-in-family
## 3745 Never-married ? Own-child
## 3746 Married-civ-spouse Farming-fishing Husband
## 3747 Separated Priv-house-serv Unmarried
## 3748 Divorced ? Unmarried
## 3749 Married-civ-spouse Craft-repair Husband
## 3750 Married-civ-spouse Adm-clerical Husband
## 3751 Divorced Exec-managerial Not-in-family
## 3752 Married-civ-spouse Tech-support Husband
## 3753 Married-civ-spouse Machine-op-inspct Husband
## 3754 Never-married Adm-clerical Not-in-family
## 3755 Married-civ-spouse Sales Husband
## 3756 Never-married Protective-serv Other-relative
## 3757 Divorced Adm-clerical Unmarried
## 3758 Married-civ-spouse Farming-fishing Wife
## 3759 Married-civ-spouse Transport-moving Husband
## 3760 Married-civ-spouse ? Husband
## 3761 Married-civ-spouse Craft-repair Husband
## 3762 Never-married Prof-specialty Not-in-family
## 3763 Never-married Other-service Not-in-family
## 3764 Never-married Adm-clerical Not-in-family
## 3765 Never-married Handlers-cleaners Own-child
## 3766 Divorced Sales Unmarried
## 3767 Married-civ-spouse Protective-serv Husband
## 3768 Never-married Adm-clerical Unmarried
## 3769 Married-civ-spouse Other-service Husband
## 3770 Never-married Other-service Not-in-family
## 3771 Never-married Prof-specialty Unmarried
## 3772 Divorced Priv-house-serv Own-child
## 3773 Married-civ-spouse Transport-moving Husband
## 3774 Widowed ? Not-in-family
## 3775 Married-civ-spouse Exec-managerial Husband
## 3776 Never-married ? Own-child
## 3777 Divorced Adm-clerical Not-in-family
## 3778 Never-married Prof-specialty Not-in-family
## 3779 Married-civ-spouse Protective-serv Husband
## 3780 Divorced Transport-moving Unmarried
## 3781 Divorced Adm-clerical Unmarried
## 3782 Never-married Adm-clerical Not-in-family
## 3783 Widowed Craft-repair Not-in-family
## 3784 Married-civ-spouse Sales Husband
## 3785 Married-civ-spouse Craft-repair Husband
## 3786 Never-married Transport-moving Not-in-family
## 3787 Married-civ-spouse Exec-managerial Husband
## 3788 Married-AF-spouse Protective-serv Husband
## 3789 Married-civ-spouse Exec-managerial Husband
## 3790 Married-civ-spouse Exec-managerial Husband
## 3791 Divorced Sales Not-in-family
## 3792 Never-married Exec-managerial Own-child
## 3793 Married-civ-spouse Adm-clerical Husband
## 3794 Never-married Other-service Own-child
## 3795 Never-married Craft-repair Not-in-family
## 3796 Married-civ-spouse Transport-moving Husband
## 3797 Never-married Machine-op-inspct Not-in-family
## 3798 Married-civ-spouse Transport-moving Husband
## 3799 Never-married Sales Other-relative
## 3800 Married-civ-spouse Craft-repair Husband
## 3801 Married-civ-spouse Protective-serv Husband
## 3802 Never-married Other-service Own-child
## 3803 Never-married Adm-clerical Own-child
## 3804 Never-married Other-service Own-child
## 3805 Never-married Other-service Other-relative
## 3806 Never-married ? Own-child
## 3807 Married-civ-spouse Prof-specialty Husband
## 3808 Never-married Exec-managerial Own-child
## 3809 Married-civ-spouse Prof-specialty Husband
## 3810 Married-civ-spouse Sales Husband
## 3811 Never-married Prof-specialty Not-in-family
## 3812 Divorced Tech-support Unmarried
## 3813 Married-civ-spouse Protective-serv Husband
## 3814 Divorced Prof-specialty Not-in-family
## 3815 Never-married Prof-specialty Not-in-family
## 3816 Never-married Other-service Own-child
## 3817 Married-civ-spouse Prof-specialty Other-relative
## 3818 Married-civ-spouse Exec-managerial Husband
## 3819 Married-civ-spouse Craft-repair Husband
## 3820 Never-married Adm-clerical Not-in-family
## 3821 Never-married Farming-fishing Unmarried
## 3822 Married-civ-spouse Other-service Wife
## 3823 Divorced ? Not-in-family
## 3824 Separated Craft-repair Not-in-family
## 3825 Never-married Farming-fishing Own-child
## 3826 Never-married Transport-moving Own-child
## 3827 Married-civ-spouse Farming-fishing Husband
## 3828 Married-civ-spouse Transport-moving Husband
## 3829 Never-married Craft-repair Not-in-family
## 3830 Married-civ-spouse Exec-managerial Husband
## 3831 Married-civ-spouse Other-service Husband
## 3832 Married-civ-spouse Prof-specialty Husband
## 3833 Married-civ-spouse Transport-moving Husband
## 3834 Married-civ-spouse Prof-specialty Husband
## 3835 Never-married ? Not-in-family
## 3836 Never-married Other-service Not-in-family
## 3837 Married-civ-spouse Prof-specialty Husband
## 3838 Divorced Exec-managerial Not-in-family
## 3839 Married-civ-spouse Exec-managerial Husband
## 3840 Never-married Craft-repair Not-in-family
## 3841 Married-civ-spouse Adm-clerical Husband
## 3842 Separated Machine-op-inspct Unmarried
## 3843 Divorced Adm-clerical Unmarried
## 3844 Never-married ? Own-child
## 3845 Never-married Exec-managerial Own-child
## 3846 Married-civ-spouse Adm-clerical Husband
## 3847 Divorced Adm-clerical Not-in-family
## 3848 Married-civ-spouse Sales Wife
## 3849 Separated Tech-support Unmarried
## 3850 Never-married Exec-managerial Not-in-family
## 3851 Married-civ-spouse Sales Husband
## 3852 Never-married ? Own-child
## 3853 Never-married Other-service Own-child
## 3854 Never-married Other-service Not-in-family
## 3855 Married-civ-spouse Handlers-cleaners Husband
## 3856 Never-married Sales Not-in-family
## 3857 Never-married Adm-clerical Not-in-family
## 3858 Divorced Sales Not-in-family
## 3859 Never-married Prof-specialty Unmarried
## 3860 Divorced Exec-managerial Unmarried
## 3861 Married-civ-spouse Sales Husband
## 3862 Divorced Transport-moving Unmarried
## 3863 Married-civ-spouse Tech-support Husband
## 3864 Married-civ-spouse ? Husband
## 3865 Married-civ-spouse Craft-repair Husband
## 3866 Married-civ-spouse Sales Wife
## 3867 Married-civ-spouse Transport-moving Husband
## 3868 Married-civ-spouse Handlers-cleaners Husband
## 3869 Never-married Craft-repair Not-in-family
## 3870 Never-married Sales Not-in-family
## 3871 Married-civ-spouse Handlers-cleaners Husband
## 3872 Married-civ-spouse Protective-serv Husband
## 3873 Married-civ-spouse Machine-op-inspct Husband
## 3874 Never-married Sales Not-in-family
## 3875 Never-married Exec-managerial Own-child
## 3876 Separated Handlers-cleaners Not-in-family
## 3877 Married-civ-spouse Transport-moving Husband
## 3878 Divorced Exec-managerial Unmarried
## 3879 Married-civ-spouse Exec-managerial Husband
## 3880 Married-civ-spouse Machine-op-inspct Wife
## 3881 Married-civ-spouse Protective-serv Husband
## 3882 Widowed Prof-specialty Not-in-family
## 3883 Married-civ-spouse Exec-managerial Husband
## 3884 Married-civ-spouse Machine-op-inspct Husband
## 3885 Married-civ-spouse Prof-specialty Husband
## 3886 Divorced Sales Own-child
## 3887 Married-civ-spouse Tech-support Husband
## 3888 Married-civ-spouse ? Wife
## 3889 Separated Craft-repair Unmarried
## 3890 Married-civ-spouse Craft-repair Husband
## 3891 Divorced Adm-clerical Own-child
## 3892 Never-married Machine-op-inspct Own-child
## 3893 Married-civ-spouse Sales Husband
## 3894 Divorced Prof-specialty Not-in-family
## 3895 Married-civ-spouse Adm-clerical Husband
## 3896 Married-civ-spouse ? Wife
## 3897 Divorced Prof-specialty Unmarried
## 3898 Never-married ? Own-child
## 3899 Never-married Other-service Own-child
## 3900 Never-married Craft-repair Other-relative
## 3901 Married-civ-spouse Exec-managerial Husband
## 3902 Never-married ? Own-child
## 3903 Never-married ? Own-child
## 3904 Never-married Other-service Not-in-family
## 3905 Married-civ-spouse Adm-clerical Husband
## 3906 Married-civ-spouse Handlers-cleaners Husband
## 3907 Married-civ-spouse Prof-specialty Husband
## 3908 Never-married Adm-clerical Not-in-family
## 3909 Married-civ-spouse Other-service Husband
## 3910 Married-civ-spouse Exec-managerial Husband
## 3911 Divorced Sales Unmarried
## 3912 Married-civ-spouse Craft-repair Husband
## 3913 Married-civ-spouse Prof-specialty Husband
## 3914 Married-civ-spouse Exec-managerial Husband
## 3915 Married-civ-spouse Craft-repair Husband
## 3916 Married-civ-spouse Craft-repair Husband
## 3917 Never-married ? Not-in-family
## 3918 Never-married Other-service Own-child
## 3919 Married-civ-spouse Prof-specialty Husband
## 3920 Never-married Transport-moving Own-child
## 3921 Married-civ-spouse Protective-serv Husband
## 3922 Divorced Craft-repair Not-in-family
## 3923 Never-married Adm-clerical Own-child
## 3924 Married-civ-spouse Other-service Husband
## 3925 Married-civ-spouse Adm-clerical Husband
## 3926 Married-civ-spouse Sales Husband
## 3927 Divorced Prof-specialty Not-in-family
## 3928 Married-civ-spouse Exec-managerial Husband
## 3929 Married-civ-spouse Handlers-cleaners Husband
## 3930 Married-civ-spouse Craft-repair Husband
## 3931 Never-married Sales Own-child
## 3932 Widowed Other-service Unmarried
## 3933 Married-civ-spouse Craft-repair Husband
## 3934 Married-civ-spouse Craft-repair Husband
## 3935 Divorced Prof-specialty Not-in-family
## 3936 Never-married Sales Own-child
## 3937 Divorced Adm-clerical Unmarried
## 3938 Separated Prof-specialty Not-in-family
## 3939 Divorced Craft-repair Unmarried
## 3940 Married-civ-spouse Machine-op-inspct Husband
## 3941 Never-married Prof-specialty Not-in-family
## 3942 Married-civ-spouse Machine-op-inspct Husband
## 3943 Never-married ? Own-child
## 3944 Never-married Adm-clerical Unmarried
## 3945 Married-civ-spouse Machine-op-inspct Husband
## 3946 Married-civ-spouse Exec-managerial Husband
## 3947 Never-married Tech-support Not-in-family
## 3948 Divorced ? Not-in-family
## 3949 Married-civ-spouse Exec-managerial Husband
## 3950 Separated ? Unmarried
## 3951 Divorced ? Unmarried
## 3952 Never-married Prof-specialty Not-in-family
## 3953 Married-civ-spouse Tech-support Husband
## 3954 Married-civ-spouse Transport-moving Husband
## 3955 Married-civ-spouse Sales Husband
## 3956 Never-married Machine-op-inspct Other-relative
## 3957 Divorced Prof-specialty Not-in-family
## 3958 Divorced Prof-specialty Not-in-family
## 3959 Married-civ-spouse Craft-repair Husband
## 3960 Married-civ-spouse Craft-repair Husband
## 3961 Divorced Prof-specialty Unmarried
## 3962 Married-civ-spouse Craft-repair Husband
## 3963 Married-civ-spouse Craft-repair Husband
## 3964 Widowed ? Not-in-family
## 3965 Married-civ-spouse Prof-specialty Husband
## 3966 Married-civ-spouse Transport-moving Wife
## 3967 Married-civ-spouse Prof-specialty Wife
## 3968 Married-civ-spouse Craft-repair Husband
## 3969 Married-civ-spouse Transport-moving Husband
## 3970 Divorced ? Not-in-family
## 3971 Never-married Adm-clerical Not-in-family
## 3972 Divorced Handlers-cleaners Not-in-family
## 3973 Never-married Handlers-cleaners Own-child
## 3974 Never-married Adm-clerical Own-child
## 3975 Separated Adm-clerical Not-in-family
## 3976 Never-married Exec-managerial Not-in-family
## 3977 Married-civ-spouse Farming-fishing Husband
## 3978 Never-married Adm-clerical Own-child
## 3979 Married-civ-spouse Sales Husband
## 3980 Divorced Sales Not-in-family
## 3981 Married-civ-spouse ? Husband
## 3982 Never-married Machine-op-inspct Not-in-family
## 3983 Divorced Prof-specialty Unmarried
## 3984 Married-civ-spouse Exec-managerial Husband
## 3985 Divorced Other-service Not-in-family
## 3986 Never-married Other-service Own-child
## 3987 Separated Adm-clerical Not-in-family
## 3988 Married-civ-spouse Other-service Husband
## 3989 Married-civ-spouse Machine-op-inspct Husband
## 3990 Never-married Protective-serv Own-child
## 3991 Widowed ? Not-in-family
## 3992 Never-married Adm-clerical Own-child
## 3993 Divorced Craft-repair Other-relative
## 3994 Married-spouse-absent Tech-support Own-child
## 3995 Separated Exec-managerial Not-in-family
## 3996 Never-married Adm-clerical Unmarried
## 3997 Never-married Other-service Not-in-family
## 3998 Married-civ-spouse Craft-repair Husband
## 3999 Married-civ-spouse Transport-moving Husband
## 4000 Married-civ-spouse Craft-repair Husband
## 4001 Married-civ-spouse Sales Husband
## 4002 Never-married Machine-op-inspct Own-child
## 4003 Never-married Sales Own-child
## 4004 Never-married ? Not-in-family
## 4005 Never-married Prof-specialty Not-in-family
## 4006 Married-civ-spouse Craft-repair Husband
## 4007 Never-married Sales Not-in-family
## 4008 Divorced Priv-house-serv Not-in-family
## 4009 Married-civ-spouse Handlers-cleaners Husband
## 4010 Divorced Prof-specialty Not-in-family
## 4011 Never-married Adm-clerical Own-child
## 4012 Never-married Sales Not-in-family
## 4013 Married-civ-spouse Handlers-cleaners Husband
## 4014 Never-married Adm-clerical Own-child
## 4015 Never-married Protective-serv Own-child
## 4016 Married-civ-spouse Exec-managerial Husband
## 4017 Divorced Machine-op-inspct Own-child
## 4018 Married-civ-spouse ? Husband
## 4019 Married-civ-spouse ? Husband
## 4020 Never-married Sales Not-in-family
## 4021 Divorced Adm-clerical Unmarried
## 4022 Never-married ? Own-child
## 4023 Divorced Sales Own-child
## 4024 Never-married Adm-clerical Other-relative
## 4025 Married-civ-spouse Tech-support Husband
## 4026 Married-civ-spouse Craft-repair Husband
## 4027 Never-married Exec-managerial Not-in-family
## 4028 Married-civ-spouse Farming-fishing Husband
## 4029 Divorced Other-service Not-in-family
## 4030 Married-civ-spouse Exec-managerial Husband
## 4031 Never-married Protective-serv Not-in-family
## 4032 Married-civ-spouse Prof-specialty Husband
## 4033 Married-civ-spouse Craft-repair Husband
## 4034 Never-married Other-service Not-in-family
## 4035 Never-married Craft-repair Not-in-family
## 4036 Separated Prof-specialty Unmarried
## 4037 Never-married Exec-managerial Own-child
## 4038 Divorced Other-service Not-in-family
## 4039 Separated Transport-moving Own-child
## 4040 Divorced Craft-repair Not-in-family
## 4041 Never-married Exec-managerial Not-in-family
## 4042 Divorced Adm-clerical Unmarried
## 4043 Married-civ-spouse Handlers-cleaners Wife
## 4044 Divorced Other-service Not-in-family
## 4045 Never-married Sales Own-child
## 4046 Married-civ-spouse Machine-op-inspct Husband
## 4047 Never-married Machine-op-inspct Other-relative
## 4048 Widowed Other-service Unmarried
## 4049 Married-civ-spouse Craft-repair Husband
## 4050 Divorced Craft-repair Not-in-family
## 4051 Married-civ-spouse Sales Husband
## 4052 Never-married Prof-specialty Not-in-family
## 4053 Divorced Transport-moving Not-in-family
## 4054 Married-civ-spouse Craft-repair Husband
## 4055 Married-civ-spouse Exec-managerial Husband
## 4056 Married-civ-spouse Prof-specialty Husband
## 4057 Married-civ-spouse Handlers-cleaners Husband
## 4058 Never-married Adm-clerical Unmarried
## 4059 Married-civ-spouse Craft-repair Husband
## 4060 Never-married Adm-clerical Own-child
## 4061 Married-civ-spouse Sales Husband
## 4062 Divorced Sales Own-child
## 4063 Never-married Sales Own-child
## 4064 Divorced Adm-clerical Not-in-family
## 4065 Divorced Sales Not-in-family
## 4066 Married-civ-spouse Sales Husband
## 4067 Married-civ-spouse Other-service Wife
## 4068 Divorced Handlers-cleaners Not-in-family
## 4069 Never-married Exec-managerial Own-child
## 4070 Never-married Other-service Unmarried
## 4071 Never-married Handlers-cleaners Own-child
## 4072 Divorced Adm-clerical Unmarried
## 4073 Never-married ? Unmarried
## 4074 Divorced Prof-specialty Unmarried
## 4075 Never-married Adm-clerical Not-in-family
## 4076 Married-civ-spouse Exec-managerial Husband
## 4077 Never-married Craft-repair Own-child
## 4078 Married-civ-spouse Sales Husband
## 4079 Never-married Tech-support Own-child
## 4080 Married-civ-spouse Sales Husband
## 4081 Never-married ? Own-child
## 4082 Never-married Handlers-cleaners Own-child
## 4083 Married-civ-spouse Craft-repair Husband
## 4084 Divorced Other-service Own-child
## 4085 Divorced Craft-repair Unmarried
## 4086 Divorced Other-service Other-relative
## 4087 Married-civ-spouse ? Husband
## 4088 Married-civ-spouse Exec-managerial Husband
## 4089 Married-civ-spouse Prof-specialty Husband
## 4090 Never-married Adm-clerical Own-child
## 4091 Never-married ? Own-child
## 4092 Divorced Craft-repair Not-in-family
## 4093 Never-married Prof-specialty Own-child
## 4094 Married-civ-spouse Prof-specialty Husband
## 4095 Married-civ-spouse Machine-op-inspct Husband
## 4096 Married-civ-spouse Exec-managerial Husband
## 4097 Married-civ-spouse Handlers-cleaners Husband
## 4098 Married-civ-spouse Prof-specialty Husband
## 4099 Never-married Handlers-cleaners Own-child
## 4100 Married-civ-spouse Transport-moving Husband
## 4101 Never-married Craft-repair Unmarried
## 4102 Married-civ-spouse Other-service Husband
## 4103 Married-civ-spouse Transport-moving Husband
## 4104 Never-married Handlers-cleaners Not-in-family
## 4105 Married-civ-spouse Sales Husband
## 4106 Divorced Handlers-cleaners Not-in-family
## 4107 Never-married Sales Not-in-family
## 4108 Married-civ-spouse Other-service Not-in-family
## 4109 Never-married Sales Not-in-family
## 4110 Widowed ? Other-relative
## 4111 Never-married Adm-clerical Not-in-family
## 4112 Divorced Transport-moving Not-in-family
## 4113 Divorced Protective-serv Unmarried
## 4114 Never-married Other-service Not-in-family
## 4115 Never-married Adm-clerical Not-in-family
## 4116 Married-civ-spouse Prof-specialty Husband
## 4117 Married-civ-spouse Transport-moving Husband
## 4118 Never-married Tech-support Own-child
## 4119 Never-married Sales Not-in-family
## 4120 Married-civ-spouse Transport-moving Husband
## 4121 Divorced Adm-clerical Not-in-family
## 4122 Married-civ-spouse Adm-clerical Husband
## 4123 Married-civ-spouse Machine-op-inspct Husband
## 4124 Divorced Other-service Own-child
## 4125 Never-married Sales Not-in-family
## 4126 Married-civ-spouse Transport-moving Husband
## 4127 Married-civ-spouse Exec-managerial Husband
## 4128 Married-civ-spouse Prof-specialty Husband
## 4129 Never-married Prof-specialty Own-child
## 4130 Never-married Other-service Not-in-family
## 4131 Never-married Craft-repair Unmarried
## 4132 Married-civ-spouse Exec-managerial Husband
## 4133 Married-civ-spouse Farming-fishing Husband
## 4134 Married-civ-spouse Craft-repair Husband
## 4135 Divorced Adm-clerical Unmarried
## 4136 Never-married Adm-clerical Unmarried
## 4137 Married-civ-spouse Exec-managerial Husband
## 4138 Married-civ-spouse Craft-repair Husband
## 4139 Never-married Adm-clerical Own-child
## 4140 Never-married Other-service Own-child
## 4141 Married-civ-spouse Protective-serv Husband
## 4142 Divorced Machine-op-inspct Not-in-family
## 4143 Never-married Adm-clerical Not-in-family
## 4144 Married-civ-spouse Exec-managerial Husband
## 4145 Divorced Craft-repair Unmarried
## 4146 Married-civ-spouse Prof-specialty Wife
## 4147 Married-civ-spouse Craft-repair Husband
## 4148 Married-civ-spouse ? Husband
## 4149 Married-civ-spouse Farming-fishing Husband
## 4150 Never-married Other-service Own-child
## 4151 Divorced Adm-clerical Not-in-family
## 4152 Divorced Prof-specialty Unmarried
## 4153 Never-married ? Own-child
## 4154 Never-married Sales Not-in-family
## 4155 Married-civ-spouse Prof-specialty Husband
## 4156 Never-married ? Other-relative
## 4157 Never-married Adm-clerical Own-child
## 4158 Divorced Handlers-cleaners Not-in-family
## 4159 Divorced Priv-house-serv Unmarried
## 4160 Married-civ-spouse Sales Husband
## 4161 Married-civ-spouse Craft-repair Own-child
## 4162 Married-civ-spouse Craft-repair Husband
## 4163 Married-civ-spouse Craft-repair Husband
## 4164 Married-spouse-absent Tech-support Unmarried
## 4165 Married-civ-spouse Other-service Wife
## 4166 Married-civ-spouse Exec-managerial Wife
## 4167 Never-married Other-service Own-child
## 4168 Married-civ-spouse Exec-managerial Husband
## 4169 Never-married Other-service Not-in-family
## 4170 Married-civ-spouse ? Wife
## 4171 Married-civ-spouse Sales Husband
## 4172 Divorced Other-service Unmarried
## 4173 Married-civ-spouse Tech-support Husband
## 4174 Never-married Farming-fishing Own-child
## 4175 Never-married ? Own-child
## 4176 Separated Other-service Not-in-family
## 4177 Married-civ-spouse Craft-repair Husband
## 4178 Married-civ-spouse Exec-managerial Husband
## 4179 Married-civ-spouse Adm-clerical Husband
## 4180 Never-married Prof-specialty Not-in-family
## 4181 Never-married Transport-moving Own-child
## 4182 Widowed Other-service Not-in-family
## 4183 Married-civ-spouse Sales Husband
## 4184 Never-married Craft-repair Not-in-family
## 4185 Never-married Prof-specialty Own-child
## 4186 Married-civ-spouse Adm-clerical Husband
## 4187 Married-civ-spouse Craft-repair Husband
## 4188 Divorced Machine-op-inspct Unmarried
## 4189 Married-civ-spouse Transport-moving Husband
## 4190 Married-civ-spouse Exec-managerial Husband
## 4191 Never-married Craft-repair Not-in-family
## 4192 Never-married Machine-op-inspct Not-in-family
## 4193 Married-civ-spouse Machine-op-inspct Husband
## 4194 Never-married Sales Own-child
## 4195 Never-married Craft-repair Own-child
## 4196 Never-married Craft-repair Not-in-family
## 4197 Married-civ-spouse Prof-specialty Husband
## 4198 Married-civ-spouse Protective-serv Husband
## 4199 Married-civ-spouse Machine-op-inspct Wife
## 4200 Never-married Other-service Not-in-family
## 4201 Never-married ? Own-child
## 4202 Married-civ-spouse Machine-op-inspct Husband
## 4203 Divorced Exec-managerial Not-in-family
## 4204 Separated Exec-managerial Not-in-family
## 4205 Never-married Farming-fishing Not-in-family
## 4206 Never-married Other-service Own-child
## 4207 Widowed Machine-op-inspct Not-in-family
## 4208 Divorced Sales Not-in-family
## 4209 Never-married Prof-specialty Not-in-family
## 4210 Never-married Tech-support Own-child
## 4211 Married-civ-spouse Prof-specialty Husband
## 4212 Married-civ-spouse Farming-fishing Husband
## 4213 Never-married ? Not-in-family
## 4214 Never-married Machine-op-inspct Own-child
## 4215 Divorced Craft-repair Not-in-family
## 4216 Never-married ? Not-in-family
## 4217 Never-married Sales Other-relative
## 4218 Never-married ? Own-child
## 4219 Never-married Sales Not-in-family
## 4220 Never-married Prof-specialty Not-in-family
## 4221 Never-married Adm-clerical Not-in-family
## 4222 Divorced Sales Unmarried
## 4223 Divorced Craft-repair Not-in-family
## 4224 Never-married Craft-repair Own-child
## 4225 Never-married Other-service Own-child
## 4226 Separated Adm-clerical Not-in-family
## 4227 Divorced Other-service Unmarried
## 4228 Never-married Other-service Not-in-family
## 4229 Divorced Adm-clerical Unmarried
## 4230 Married-civ-spouse Transport-moving Wife
## 4231 Married-civ-spouse Craft-repair Husband
## 4232 Divorced Exec-managerial Unmarried
## 4233 Married-civ-spouse Exec-managerial Husband
## 4234 Never-married Prof-specialty Not-in-family
## 4235 Widowed Exec-managerial Unmarried
## 4236 Never-married Prof-specialty Not-in-family
## 4237 Married-civ-spouse Prof-specialty Husband
## 4238 Never-married Transport-moving Own-child
## 4239 Married-civ-spouse Exec-managerial Husband
## 4240 Separated Sales Unmarried
## 4241 Never-married ? Not-in-family
## 4242 Never-married Prof-specialty Not-in-family
## 4243 Divorced Craft-repair Own-child
## 4244 Never-married Craft-repair Not-in-family
## 4245 Married-civ-spouse Sales Husband
## 4246 Never-married Craft-repair Not-in-family
## 4247 Married-civ-spouse Prof-specialty Wife
## 4248 Never-married Handlers-cleaners Own-child
## 4249 Never-married Other-service Not-in-family
## 4250 Married-civ-spouse Sales Husband
## 4251 Never-married Machine-op-inspct Own-child
## 4252 Divorced Tech-support Not-in-family
## 4253 Married-spouse-absent Craft-repair Not-in-family
## 4254 Divorced Adm-clerical Unmarried
## 4255 Never-married Handlers-cleaners Not-in-family
## 4256 Married-civ-spouse Tech-support Husband
## 4257 Married-civ-spouse Adm-clerical Other-relative
## 4258 Married-civ-spouse Craft-repair Husband
## 4259 Married-civ-spouse Transport-moving Husband
## 4260 Never-married Other-service Not-in-family
## 4261 Divorced Sales Not-in-family
## 4262 Never-married Other-service Not-in-family
## 4263 Never-married Adm-clerical Own-child
## 4264 Married-civ-spouse Farming-fishing Wife
## 4265 Married-civ-spouse Exec-managerial Husband
## 4266 Married-civ-spouse Exec-managerial Husband
## 4267 Married-civ-spouse Prof-specialty Husband
## 4268 Married-civ-spouse Craft-repair Husband
## 4269 Never-married Transport-moving Not-in-family
## 4270 Never-married Other-service Other-relative
## 4271 Divorced Adm-clerical Not-in-family
## 4272 Married-civ-spouse Exec-managerial Husband
## 4273 Never-married ? Own-child
## 4274 Widowed Other-service Unmarried
## 4275 Separated Prof-specialty Not-in-family
## 4276 Married-civ-spouse Handlers-cleaners Husband
## 4277 Never-married Sales Not-in-family
## 4278 Never-married Sales Own-child
## 4279 Separated Adm-clerical Unmarried
## 4280 Divorced Prof-specialty Unmarried
## 4281 Married-civ-spouse Handlers-cleaners Husband
## 4282 Widowed Other-service Unmarried
## 4283 Married-civ-spouse Sales Wife
## 4284 Married-civ-spouse Other-service Husband
## 4285 Never-married Machine-op-inspct Own-child
## 4286 Married-civ-spouse Machine-op-inspct Husband
## 4287 Never-married Adm-clerical Other-relative
## 4288 Married-civ-spouse Machine-op-inspct Husband
## 4289 Divorced ? Not-in-family
## 4290 Never-married Sales Not-in-family
## 4291 Married-civ-spouse Craft-repair Husband
## 4292 Married-civ-spouse Machine-op-inspct Husband
## 4293 Never-married Exec-managerial Unmarried
## 4294 Divorced Prof-specialty Not-in-family
## 4295 Never-married Prof-specialty Not-in-family
## 4296 Married-civ-spouse Sales Husband
## 4297 Never-married Other-service Own-child
## 4298 Married-civ-spouse Exec-managerial Husband
## 4299 Never-married Other-service Not-in-family
## 4300 Never-married ? Own-child
## 4301 Never-married Sales Not-in-family
## 4302 Never-married Farming-fishing Unmarried
## 4303 Married-civ-spouse Other-service Husband
## 4304 Married-civ-spouse Craft-repair Husband
## 4305 Married-civ-spouse Craft-repair Husband
## 4306 Never-married Adm-clerical Own-child
## 4307 Married-civ-spouse Tech-support Husband
## 4308 Married-civ-spouse Machine-op-inspct Husband
## 4309 Married-civ-spouse Farming-fishing Husband
## 4310 Widowed ? Not-in-family
## 4311 Never-married Tech-support Own-child
## 4312 Married-civ-spouse Sales Husband
## 4313 Married-civ-spouse Prof-specialty Husband
## 4314 Never-married Craft-repair Own-child
## 4315 Divorced Exec-managerial Not-in-family
## 4316 Married-civ-spouse ? Husband
## 4317 Widowed Craft-repair Not-in-family
## 4318 Separated Adm-clerical Unmarried
## 4319 Never-married Adm-clerical Not-in-family
## 4320 Married-civ-spouse Exec-managerial Husband
## 4321 Never-married Craft-repair Own-child
## 4322 Married-civ-spouse Adm-clerical Husband
## 4323 Divorced Exec-managerial Not-in-family
## 4324 Never-married Craft-repair Not-in-family
## 4325 Separated Prof-specialty Own-child
## 4326 Never-married Craft-repair Not-in-family
## 4327 Married-civ-spouse Transport-moving Husband
## 4328 Married-civ-spouse Exec-managerial Husband
## 4329 Never-married Handlers-cleaners Own-child
## 4330 Divorced Exec-managerial Not-in-family
## 4331 Married-civ-spouse Protective-serv Husband
## 4332 Married-civ-spouse Exec-managerial Husband
## 4333 Married-civ-spouse Prof-specialty Husband
## 4334 Never-married Exec-managerial Not-in-family
## 4335 Married-civ-spouse Sales Husband
## 4336 Married-civ-spouse Prof-specialty Husband
## 4337 Never-married Other-service Unmarried
## 4338 Never-married ? Own-child
## 4339 Married-civ-spouse Prof-specialty Husband
## 4340 Married-civ-spouse Prof-specialty Husband
## 4341 Married-spouse-absent Adm-clerical Own-child
## 4342 Married-civ-spouse Craft-repair Husband
## 4343 Never-married Sales Own-child
## 4344 Married-civ-spouse Prof-specialty Husband
## 4345 Married-civ-spouse Transport-moving Husband
## 4346 Married-civ-spouse Exec-managerial Husband
## 4347 Never-married Adm-clerical Unmarried
## 4348 Never-married Exec-managerial Unmarried
## 4349 Divorced Handlers-cleaners Not-in-family
## 4350 Married-civ-spouse Exec-managerial Husband
## 4351 Never-married Handlers-cleaners Own-child
## 4352 Married-civ-spouse Machine-op-inspct Husband
## 4353 Divorced Transport-moving Not-in-family
## 4354 Married-civ-spouse Prof-specialty Wife
## 4355 Married-civ-spouse Prof-specialty Husband
## 4356 Married-civ-spouse Craft-repair Husband
## 4357 Divorced Exec-managerial Unmarried
## 4358 Never-married Craft-repair Own-child
## 4359 Never-married Adm-clerical Not-in-family
## 4360 Never-married Prof-specialty Not-in-family
## 4361 Never-married Prof-specialty Own-child
## 4362 Married-civ-spouse Handlers-cleaners Husband
## 4363 Never-married Craft-repair Not-in-family
## 4364 Married-civ-spouse Transport-moving Husband
## 4365 Never-married Exec-managerial Unmarried
## 4366 Married-civ-spouse Sales Husband
## 4367 Married-civ-spouse Sales Husband
## 4368 Married-civ-spouse Machine-op-inspct Husband
## 4369 Married-civ-spouse Exec-managerial Wife
## 4370 Never-married ? Own-child
## 4371 Married-civ-spouse Machine-op-inspct Husband
## 4372 Married-civ-spouse Transport-moving Husband
## 4373 Never-married Exec-managerial Own-child
## 4374 Never-married Adm-clerical Own-child
## 4375 Married-civ-spouse Prof-specialty Husband
## 4376 Never-married Craft-repair Not-in-family
## 4377 Married-civ-spouse Exec-managerial Husband
## 4378 Married-civ-spouse Prof-specialty Husband
## 4379 Married-civ-spouse Machine-op-inspct Wife
## 4380 Never-married Sales Own-child
## 4381 Married-civ-spouse Exec-managerial Husband
## 4382 Married-spouse-absent Exec-managerial Not-in-family
## 4383 Divorced Machine-op-inspct Not-in-family
## 4384 Never-married Sales Other-relative
## 4385 Married-civ-spouse Machine-op-inspct Husband
## 4386 Married-civ-spouse Craft-repair Husband
## 4387 Separated Craft-repair Not-in-family
## 4388 Divorced Adm-clerical Not-in-family
## 4389 Married-civ-spouse Craft-repair Husband
## 4390 Married-civ-spouse Craft-repair Husband
## 4391 Married-civ-spouse Prof-specialty Wife
## 4392 Never-married Craft-repair Other-relative
## 4393 Married-civ-spouse Other-service Husband
## 4394 Never-married ? Own-child
## 4395 Never-married Prof-specialty Not-in-family
## 4396 Never-married Handlers-cleaners Other-relative
## 4397 Never-married Sales Not-in-family
## 4398 Never-married Prof-specialty Not-in-family
## 4399 Never-married Sales Own-child
## 4400 Never-married Exec-managerial Own-child
## 4401 Divorced Other-service Unmarried
## 4402 Married-civ-spouse Exec-managerial Wife
## 4403 Never-married Sales Own-child
## 4404 Married-civ-spouse Sales Husband
## 4405 Married-civ-spouse Craft-repair Husband
## 4406 Married-civ-spouse Exec-managerial Husband
## 4407 Never-married Adm-clerical Own-child
## 4408 Married-civ-spouse Sales Husband
## 4409 Married-civ-spouse Adm-clerical Husband
## 4410 Never-married ? Other-relative
## 4411 Married-civ-spouse Transport-moving Husband
## 4412 Married-civ-spouse Prof-specialty Husband
## 4413 Divorced Other-service Own-child
## 4414 Never-married ? Own-child
## 4415 Never-married Adm-clerical Not-in-family
## 4416 Divorced Craft-repair Not-in-family
## 4417 Never-married Adm-clerical Own-child
## 4418 Married-civ-spouse Adm-clerical Husband
## 4419 Widowed Exec-managerial Unmarried
## 4420 Never-married Other-service Other-relative
## 4421 Separated Machine-op-inspct Unmarried
## 4422 Married-civ-spouse Exec-managerial Husband
## 4423 Widowed ? Not-in-family
## 4424 Divorced Craft-repair Unmarried
## 4425 Divorced Sales Unmarried
## 4426 Married-civ-spouse Craft-repair Husband
## 4427 Married-civ-spouse Transport-moving Husband
## 4428 Never-married Other-service Not-in-family
## 4429 Married-civ-spouse Adm-clerical Husband
## 4430 Married-civ-spouse Handlers-cleaners Husband
## 4431 Married-civ-spouse Adm-clerical Wife
## 4432 Never-married Adm-clerical Not-in-family
## 4433 Divorced Exec-managerial Not-in-family
## 4434 Married-civ-spouse Prof-specialty Husband
## 4435 Never-married Tech-support Not-in-family
## 4436 Divorced ? Not-in-family
## 4437 Married-civ-spouse Exec-managerial Husband
## 4438 Never-married ? Not-in-family
## 4439 Married-civ-spouse Prof-specialty Husband
## 4440 Never-married Sales Not-in-family
## 4441 Married-civ-spouse Prof-specialty Husband
## 4442 Married-civ-spouse Prof-specialty Husband
## 4443 Never-married Exec-managerial Not-in-family
## 4444 Married-civ-spouse Sales Husband
## 4445 Never-married Machine-op-inspct Own-child
## 4446 Married-civ-spouse Exec-managerial Husband
## 4447 Separated Other-service Not-in-family
## 4448 Never-married Exec-managerial Own-child
## 4449 Divorced Adm-clerical Not-in-family
## 4450 Divorced Craft-repair Not-in-family
## 4451 Never-married Sales Not-in-family
## 4452 Never-married Craft-repair Not-in-family
## 4453 Never-married Adm-clerical Own-child
## 4454 Married-civ-spouse Adm-clerical Wife
## 4455 Never-married Sales Other-relative
## 4456 Married-civ-spouse Craft-repair Husband
## 4457 Married-civ-spouse Prof-specialty Husband
## 4458 Divorced Protective-serv Own-child
## 4459 Divorced Farming-fishing Unmarried
## 4460 Never-married Prof-specialty Not-in-family
## 4461 Divorced ? Unmarried
## 4462 Never-married Other-service Own-child
## 4463 Married-civ-spouse Sales Husband
## 4464 Widowed Adm-clerical Unmarried
## 4465 Never-married ? Not-in-family
## 4466 Married-civ-spouse Exec-managerial Husband
## 4467 Married-civ-spouse Prof-specialty Wife
## 4468 Married-civ-spouse Sales Husband
## 4469 Never-married Machine-op-inspct Own-child
## 4470 Never-married Sales Own-child
## 4471 Married-civ-spouse Transport-moving Husband
## 4472 Married-civ-spouse Adm-clerical Husband
## 4473 Married-civ-spouse Adm-clerical Wife
## 4474 Married-civ-spouse Transport-moving Husband
## 4475 Married-civ-spouse Prof-specialty Husband
## 4476 Married-civ-spouse Prof-specialty Husband
## 4477 Never-married Craft-repair Not-in-family
## 4478 Married-civ-spouse Other-service Husband
## 4479 Divorced Exec-managerial Unmarried
## 4480 Never-married Prof-specialty Not-in-family
## 4481 Married-civ-spouse Sales Husband
## 4482 Never-married Prof-specialty Not-in-family
## 4483 Never-married Exec-managerial Unmarried
## 4484 Never-married Sales Not-in-family
## 4485 Widowed Adm-clerical Unmarried
## 4486 Married-spouse-absent Adm-clerical Unmarried
## 4487 Married-civ-spouse Craft-repair Husband
## 4488 Never-married Other-service Own-child
## 4489 Never-married Prof-specialty Not-in-family
## 4490 Separated Other-service Not-in-family
## 4491 Never-married Sales Not-in-family
## 4492 Married-civ-spouse Sales Husband
## 4493 Married-civ-spouse Prof-specialty Wife
## 4494 Divorced Sales Own-child
## 4495 Married-civ-spouse Craft-repair Husband
## 4496 Married-civ-spouse Exec-managerial Husband
## 4497 Married-civ-spouse Farming-fishing Husband
## 4498 Married-civ-spouse Craft-repair Husband
## 4499 Divorced Other-service Unmarried
## 4500 Never-married ? Own-child
## 4501 Separated ? Not-in-family
## 4502 Never-married Handlers-cleaners Not-in-family
## 4503 Married-civ-spouse Exec-managerial Husband
## 4504 Never-married Prof-specialty Not-in-family
## 4505 Divorced Prof-specialty Unmarried
## 4506 Never-married Transport-moving Not-in-family
## 4507 Never-married Handlers-cleaners Own-child
## 4508 Married-civ-spouse Machine-op-inspct Husband
## 4509 Never-married Handlers-cleaners Not-in-family
## 4510 Never-married Machine-op-inspct Own-child
## 4511 Divorced Other-service Unmarried
## 4512 Never-married Exec-managerial Not-in-family
## 4513 Married-civ-spouse Craft-repair Other-relative
## 4514 Never-married Other-service Own-child
## 4515 Divorced Exec-managerial Other-relative
## 4516 Never-married Adm-clerical Own-child
## 4517 Never-married Adm-clerical Not-in-family
## 4518 Married-civ-spouse Sales Husband
## 4519 Married-civ-spouse Transport-moving Husband
## 4520 Never-married ? Not-in-family
## 4521 Married-civ-spouse Other-service Husband
## 4522 Married-civ-spouse Handlers-cleaners Husband
## 4523 Never-married Adm-clerical Not-in-family
## 4524 Married-civ-spouse Prof-specialty Husband
## 4525 Married-civ-spouse Craft-repair Husband
## 4526 Never-married Exec-managerial Not-in-family
## 4527 Married-civ-spouse Machine-op-inspct Wife
## 4528 Never-married Other-service Not-in-family
## 4529 Divorced Other-service Unmarried
## 4530 Married-civ-spouse Adm-clerical Husband
## 4531 Divorced Exec-managerial Not-in-family
## 4532 Never-married ? Own-child
## 4533 Divorced Handlers-cleaners Own-child
## 4534 Never-married Machine-op-inspct Own-child
## 4535 Never-married Handlers-cleaners Own-child
## 4536 Married-civ-spouse Sales Husband
## 4537 Never-married Prof-specialty Not-in-family
## 4538 Married-civ-spouse Prof-specialty Husband
## 4539 Never-married Sales Own-child
## 4540 Married-civ-spouse Prof-specialty Husband
## 4541 Married-civ-spouse Adm-clerical Wife
## 4542 Divorced Adm-clerical Other-relative
## 4543 Married-civ-spouse Farming-fishing Husband
## 4544 Married-civ-spouse Farming-fishing Husband
## 4545 Never-married Craft-repair Not-in-family
## 4546 Married-civ-spouse Exec-managerial Wife
## 4547 Married-civ-spouse Prof-specialty Husband
## 4548 Married-civ-spouse Craft-repair Husband
## 4549 Divorced ? Not-in-family
## 4550 Married-civ-spouse Exec-managerial Husband
## 4551 Married-civ-spouse Machine-op-inspct Husband
## 4552 Never-married Prof-specialty Not-in-family
## 4553 Married-civ-spouse Craft-repair Husband
## 4554 Never-married ? Own-child
## 4555 Never-married Other-service Own-child
## 4556 Married-civ-spouse Handlers-cleaners Husband
## 4557 Never-married Adm-clerical Unmarried
## 4558 Never-married Sales Not-in-family
## 4559 Married-civ-spouse Transport-moving Husband
## 4560 Married-civ-spouse Exec-managerial Husband
## 4561 Never-married Machine-op-inspct Not-in-family
## 4562 Married-civ-spouse Other-service Husband
## 4563 Never-married Other-service Unmarried
## 4564 Never-married Sales Not-in-family
## 4565 Divorced Adm-clerical Not-in-family
## 4566 Married-civ-spouse Craft-repair Husband
## 4567 Married-civ-spouse Sales Husband
## 4568 Married-civ-spouse Prof-specialty Husband
## 4569 Never-married Protective-serv Own-child
## 4570 Divorced Adm-clerical Unmarried
## 4571 Widowed Adm-clerical Not-in-family
## 4572 Married-civ-spouse Exec-managerial Husband
## 4573 Never-married Handlers-cleaners Not-in-family
## 4574 Never-married Adm-clerical Unmarried
## 4575 Never-married Farming-fishing Not-in-family
## 4576 Married-civ-spouse Prof-specialty Wife
## 4577 Married-civ-spouse Machine-op-inspct Wife
## 4578 Married-civ-spouse Protective-serv Husband
## 4579 Divorced Other-service Not-in-family
## 4580 Divorced Prof-specialty Unmarried
## 4581 Never-married Other-service Own-child
## 4582 Divorced Transport-moving Unmarried
## 4583 Never-married Adm-clerical Not-in-family
## 4584 Divorced Sales Unmarried
## 4585 Married-civ-spouse Sales Husband
## 4586 Never-married Other-service Not-in-family
## 4587 Never-married Adm-clerical Not-in-family
## 4588 Separated Adm-clerical Not-in-family
## 4589 Married-civ-spouse Other-service Wife
## 4590 Never-married Machine-op-inspct Not-in-family
## 4591 Married-civ-spouse Sales Husband
## 4592 Separated Craft-repair Own-child
## 4593 Married-civ-spouse ? Wife
## 4594 Married-civ-spouse Exec-managerial Husband
## 4595 Never-married Other-service Own-child
## 4596 Married-civ-spouse Sales Husband
## 4597 Never-married Craft-repair Not-in-family
## 4598 Married-civ-spouse Exec-managerial Husband
## 4599 Married-spouse-absent Prof-specialty Not-in-family
## 4600 Married-civ-spouse Machine-op-inspct Husband
## 4601 Married-civ-spouse Craft-repair Husband
## 4602 Separated Other-service Unmarried
## 4603 Separated Transport-moving Unmarried
## 4604 Married-civ-spouse Craft-repair Husband
## 4605 Separated Adm-clerical Unmarried
## 4606 Never-married Sales Own-child
## 4607 Married-civ-spouse ? Wife
## 4608 Never-married ? Unmarried
## 4609 Divorced Exec-managerial Not-in-family
## 4610 Married-civ-spouse Other-service Wife
## 4611 Married-civ-spouse Craft-repair Husband
## 4612 Married-civ-spouse Sales Husband
## 4613 Married-civ-spouse Prof-specialty Husband
## 4614 Married-civ-spouse ? Husband
## 4615 Married-civ-spouse Farming-fishing Husband
## 4616 Never-married Transport-moving Own-child
## 4617 Divorced Adm-clerical Not-in-family
## 4618 Never-married Prof-specialty Other-relative
## 4619 Married-civ-spouse Exec-managerial Husband
## 4620 Divorced Protective-serv Other-relative
## 4621 Never-married Exec-managerial Not-in-family
## 4622 Never-married ? Own-child
## 4623 Married-civ-spouse Exec-managerial Husband
## 4624 Never-married Other-service Own-child
## 4625 Never-married Sales Unmarried
## 4626 Married-civ-spouse Prof-specialty Husband
## 4627 Separated Handlers-cleaners Own-child
## 4628 Married-civ-spouse ? Husband
## 4629 Never-married Craft-repair Not-in-family
## 4630 Married-civ-spouse Adm-clerical Wife
## 4631 Married-civ-spouse Craft-repair Husband
## 4632 Never-married Adm-clerical Own-child
## 4633 Married-civ-spouse Transport-moving Husband
## 4634 Married-spouse-absent Other-service Unmarried
## 4635 Married-civ-spouse Tech-support Husband
## 4636 Never-married Prof-specialty Own-child
## 4637 Married-civ-spouse Craft-repair Husband
## 4638 Married-civ-spouse Craft-repair Husband
## 4639 Divorced Other-service Not-in-family
## 4640 Never-married Prof-specialty Not-in-family
## 4641 Never-married Craft-repair Own-child
## 4642 Married-civ-spouse Exec-managerial Husband
## 4643 Never-married Other-service Other-relative
## 4644 Widowed Other-service Unmarried
## 4645 Married-civ-spouse Other-service Husband
## 4646 Married-civ-spouse Adm-clerical Husband
## 4647 Married-civ-spouse Transport-moving Husband
## 4648 Never-married Handlers-cleaners Not-in-family
## 4649 Never-married Sales Not-in-family
## 4650 Separated Sales Unmarried
## 4651 Never-married Sales Own-child
## 4652 Married-civ-spouse Exec-managerial Husband
## 4653 Divorced Prof-specialty Unmarried
## 4654 Married-civ-spouse Craft-repair Husband
## 4655 Married-civ-spouse Handlers-cleaners Husband
## 4656 Never-married ? Not-in-family
## 4657 Married-civ-spouse Prof-specialty Wife
## 4658 Married-civ-spouse Exec-managerial Husband
## 4659 Never-married Other-service Own-child
## 4660 Married-civ-spouse Transport-moving Husband
## 4661 Never-married Other-service Not-in-family
## 4662 Married-civ-spouse Sales Husband
## 4663 Married-civ-spouse Prof-specialty Husband
## 4664 Married-civ-spouse Craft-repair Wife
## 4665 Never-married Handlers-cleaners Own-child
## 4666 Divorced Other-service Unmarried
## 4667 Married-civ-spouse Prof-specialty Husband
## 4668 Never-married Farming-fishing Own-child
## 4669 Married-civ-spouse Exec-managerial Husband
## 4670 Married-civ-spouse Prof-specialty Husband
## 4671 Married-civ-spouse Craft-repair Husband
## 4672 Married-civ-spouse Sales Husband
## 4673 Never-married Sales Not-in-family
## 4674 Married-civ-spouse Craft-repair Husband
## 4675 Married-civ-spouse Exec-managerial Husband
## 4676 Married-civ-spouse Tech-support Husband
## 4677 Never-married Sales Own-child
## 4678 Married-civ-spouse Craft-repair Husband
## 4679 Never-married Prof-specialty Not-in-family
## 4680 Never-married Exec-managerial Other-relative
## 4681 Married-civ-spouse Exec-managerial Husband
## 4682 Married-civ-spouse Exec-managerial Husband
## 4683 Married-civ-spouse Prof-specialty Husband
## 4684 Never-married Transport-moving Not-in-family
## 4685 Never-married Adm-clerical Not-in-family
## 4686 Married-civ-spouse ? Wife
## 4687 Never-married Prof-specialty Own-child
## 4688 Separated Machine-op-inspct Unmarried
## 4689 Never-married ? Own-child
## 4690 Married-civ-spouse Craft-repair Husband
## 4691 Never-married Tech-support Own-child
## 4692 Married-civ-spouse Adm-clerical Husband
## 4693 Divorced Tech-support Not-in-family
## 4694 Never-married Tech-support Own-child
## 4695 Married-civ-spouse Craft-repair Husband
## 4696 Married-civ-spouse Other-service Husband
## 4697 Married-civ-spouse Sales Husband
## 4698 Never-married Other-service Other-relative
## 4699 Married-spouse-absent Craft-repair Not-in-family
## 4700 Married-civ-spouse Craft-repair Husband
## 4701 Never-married Craft-repair Other-relative
## 4702 Never-married Handlers-cleaners Not-in-family
## 4703 Never-married Sales Own-child
## 4704 Divorced Handlers-cleaners Not-in-family
## 4705 Married-civ-spouse Transport-moving Wife
## 4706 Divorced Machine-op-inspct Unmarried
## 4707 Married-civ-spouse Farming-fishing Husband
## 4708 Married-civ-spouse Craft-repair Husband
## 4709 Never-married Other-service Own-child
## 4710 Married-civ-spouse Prof-specialty Husband
## 4711 Divorced Craft-repair Not-in-family
## 4712 Married-civ-spouse Protective-serv Husband
## 4713 Never-married Adm-clerical Own-child
## 4714 Divorced Craft-repair Unmarried
## 4715 Never-married Handlers-cleaners Own-child
## 4716 Never-married Adm-clerical Not-in-family
## 4717 Never-married Other-service Unmarried
## 4718 Married-civ-spouse Craft-repair Husband
## 4719 Widowed Other-service Unmarried
## 4720 Divorced Exec-managerial Not-in-family
## 4721 Widowed Other-service Not-in-family
## 4722 Married-civ-spouse ? Husband
## 4723 Never-married Sales Own-child
## 4724 Married-civ-spouse Craft-repair Husband
## 4725 Married-civ-spouse Sales Husband
## 4726 Never-married Craft-repair Other-relative
## 4727 Married-civ-spouse Transport-moving Husband
## 4728 Married-civ-spouse Prof-specialty Husband
## 4729 Never-married Prof-specialty Own-child
## 4730 Widowed ? Not-in-family
## 4731 Divorced Other-service Unmarried
## 4732 Divorced Craft-repair Other-relative
## 4733 Never-married Farming-fishing Unmarried
## 4734 Never-married Adm-clerical Unmarried
## 4735 Never-married Other-service Other-relative
## 4736 Never-married Sales Not-in-family
## 4737 Married-civ-spouse Exec-managerial Husband
## 4738 Never-married Machine-op-inspct Own-child
## 4739 Never-married Transport-moving Not-in-family
## 4740 Never-married Exec-managerial Not-in-family
## 4741 Never-married Prof-specialty Not-in-family
## 4742 Never-married Prof-specialty Not-in-family
## 4743 Married-civ-spouse Prof-specialty Husband
## 4744 Married-civ-spouse Sales Husband
## 4745 Never-married Exec-managerial Not-in-family
## 4746 Widowed ? Unmarried
## 4747 Never-married Sales Not-in-family
## 4748 Married-civ-spouse Prof-specialty Husband
## 4749 Divorced Exec-managerial Unmarried
## 4750 Divorced Prof-specialty Not-in-family
## 4751 Married-civ-spouse Sales Husband
## 4752 Married-civ-spouse Prof-specialty Husband
## 4753 Married-civ-spouse ? Husband
## 4754 Married-civ-spouse Craft-repair Husband
## 4755 Divorced Exec-managerial Unmarried
## 4756 Married-civ-spouse ? Husband
## 4757 Married-civ-spouse Craft-repair Husband
## 4758 Married-civ-spouse Machine-op-inspct Husband
## 4759 Never-married Craft-repair Unmarried
## 4760 Widowed Sales Unmarried
## 4761 Divorced Sales Not-in-family
## 4762 Married-civ-spouse Prof-specialty Husband
## 4763 Never-married Adm-clerical Not-in-family
## 4764 Divorced Protective-serv Not-in-family
## 4765 Divorced Prof-specialty Not-in-family
## 4766 Married-civ-spouse Machine-op-inspct Husband
## 4767 Married-civ-spouse ? Husband
## 4768 Never-married Prof-specialty Own-child
## 4769 Married-civ-spouse Tech-support Husband
## 4770 Never-married Craft-repair Not-in-family
## 4771 Never-married Other-service Own-child
## 4772 Married-civ-spouse Other-service Husband
## 4773 Divorced Other-service Other-relative
## 4774 Never-married Farming-fishing Own-child
## 4775 Never-married Tech-support Not-in-family
## 4776 Never-married Craft-repair Own-child
## 4777 Married-civ-spouse Prof-specialty Husband
## 4778 Never-married Sales Not-in-family
## 4779 Divorced ? Own-child
## 4780 Never-married Sales Own-child
## 4781 Married-civ-spouse Exec-managerial Husband
## 4782 Separated Craft-repair Own-child
## 4783 Married-civ-spouse ? Husband
## 4784 Never-married Handlers-cleaners Own-child
## 4785 Married-civ-spouse Exec-managerial Husband
## 4786 Never-married Craft-repair Other-relative
## 4787 Never-married Transport-moving Own-child
## 4788 Married-civ-spouse Machine-op-inspct Husband
## 4789 Married-civ-spouse Prof-specialty Husband
## 4790 Married-civ-spouse Other-service Husband
## 4791 Married-civ-spouse Prof-specialty Husband
## 4792 Divorced Adm-clerical Unmarried
## 4793 Married-civ-spouse Machine-op-inspct Husband
## 4794 Married-civ-spouse Handlers-cleaners Husband
## 4795 Married-civ-spouse Machine-op-inspct Husband
## 4796 Married-civ-spouse Exec-managerial Husband
## 4797 Never-married Adm-clerical Own-child
## 4798 Married-civ-spouse Sales Husband
## 4799 Never-married Transport-moving Not-in-family
## 4800 Never-married Adm-clerical Not-in-family
## 4801 Married-civ-spouse Transport-moving Husband
## 4802 Never-married ? Not-in-family
## 4803 Married-civ-spouse Machine-op-inspct Husband
## 4804 Never-married Other-service Not-in-family
## 4805 Married-civ-spouse Farming-fishing Husband
## 4806 Never-married Prof-specialty Not-in-family
## 4807 Divorced Transport-moving Not-in-family
## 4808 Divorced Sales Unmarried
## 4809 Married-civ-spouse Adm-clerical Husband
## 4810 Never-married Tech-support Own-child
## 4811 Divorced Sales Not-in-family
## 4812 Never-married Prof-specialty Not-in-family
## 4813 Divorced ? Not-in-family
## 4814 Married-civ-spouse Machine-op-inspct Husband
## 4815 Never-married Adm-clerical Own-child
## 4816 Married-civ-spouse Adm-clerical Wife
## 4817 Married-civ-spouse Exec-managerial Husband
## 4818 Never-married Exec-managerial Not-in-family
## 4819 Never-married ? Own-child
## 4820 Divorced Adm-clerical Unmarried
## 4821 Married-civ-spouse Machine-op-inspct Husband
## 4822 Divorced Sales Unmarried
## 4823 Never-married Prof-specialty Not-in-family
## 4824 Married-civ-spouse Craft-repair Husband
## 4825 Divorced Other-service Unmarried
## 4826 Married-civ-spouse Adm-clerical Husband
## 4827 Never-married Adm-clerical Unmarried
## 4828 Married-civ-spouse Craft-repair Husband
## 4829 Divorced Other-service Not-in-family
## 4830 Divorced Other-service Unmarried
## 4831 Married-spouse-absent Craft-repair Other-relative
## 4832 Widowed Adm-clerical Not-in-family
## 4833 Married-civ-spouse Craft-repair Husband
## 4834 Married-civ-spouse Sales Husband
## 4835 Divorced Adm-clerical Not-in-family
## 4836 Never-married ? Unmarried
## 4837 Divorced Prof-specialty Not-in-family
## 4838 Never-married Adm-clerical Not-in-family
## 4839 Married-civ-spouse ? Husband
## 4840 Never-married Tech-support Unmarried
## 4841 Never-married Other-service Unmarried
## 4842 Divorced Adm-clerical Other-relative
## 4843 Separated Sales Not-in-family
## 4844 Married-civ-spouse Craft-repair Husband
## 4845 Never-married Handlers-cleaners Unmarried
## 4846 Married-civ-spouse Adm-clerical Husband
## 4847 Married-civ-spouse Craft-repair Husband
## 4848 Married-civ-spouse Exec-managerial Husband
## 4849 Separated Exec-managerial Not-in-family
## 4850 Married-civ-spouse Farming-fishing Husband
## 4851 Married-civ-spouse Craft-repair Husband
## 4852 Married-civ-spouse Exec-managerial Husband
## 4853 Married-civ-spouse Protective-serv Husband
## 4854 Never-married Handlers-cleaners Own-child
## 4855 Never-married Tech-support Not-in-family
## 4856 Never-married Adm-clerical Own-child
## 4857 Never-married Sales Not-in-family
## 4858 Married-civ-spouse Exec-managerial Husband
## 4859 Married-civ-spouse Transport-moving Husband
## 4860 Never-married Farming-fishing Other-relative
## 4861 Separated Prof-specialty Unmarried
## 4862 Never-married Machine-op-inspct Other-relative
## 4863 Married-civ-spouse Transport-moving Husband
## 4864 Married-civ-spouse Exec-managerial Husband
## 4865 Never-married Craft-repair Own-child
## 4866 Married-civ-spouse Protective-serv Husband
## 4867 Never-married Craft-repair Own-child
## 4868 Married-civ-spouse Machine-op-inspct Husband
## 4869 Never-married Sales Other-relative
## 4870 Divorced Other-service Not-in-family
## 4871 Married-civ-spouse Prof-specialty Husband
## 4872 Never-married Adm-clerical Own-child
## 4873 Married-civ-spouse Prof-specialty Husband
## 4874 Married-civ-spouse Sales Husband
## 4875 Married-civ-spouse Adm-clerical Wife
## 4876 Married-civ-spouse Exec-managerial Husband
## 4877 Never-married Prof-specialty Own-child
## 4878 Divorced Adm-clerical Unmarried
## 4879 Married-civ-spouse Exec-managerial Wife
## 4880 Divorced Prof-specialty Not-in-family
## 4881 Never-married Transport-moving Not-in-family
## 4882 Never-married Craft-repair Not-in-family
## 4883 Married-civ-spouse Craft-repair Husband
## 4884 Never-married Sales Not-in-family
## 4885 Never-married ? Unmarried
## 4886 Married-civ-spouse Exec-managerial Husband
## 4887 Separated Exec-managerial Unmarried
## 4888 Never-married Adm-clerical Unmarried
## 4889 Married-civ-spouse Exec-managerial Husband
## 4890 Married-civ-spouse Craft-repair Husband
## 4891 Married-civ-spouse Farming-fishing Husband
## 4892 Never-married Other-service Not-in-family
## 4893 Never-married Other-service Own-child
## 4894 Never-married Tech-support Not-in-family
## 4895 Married-civ-spouse Exec-managerial Husband
## 4896 Never-married Farming-fishing Not-in-family
## 4897 Never-married Sales Not-in-family
## 4898 Married-civ-spouse Prof-specialty Husband
## 4899 Married-civ-spouse Prof-specialty Husband
## 4900 Never-married Prof-specialty Not-in-family
## 4901 Married-civ-spouse Craft-repair Husband
## 4902 Never-married Exec-managerial Not-in-family
## 4903 Married-civ-spouse Craft-repair Husband
## 4904 Married-civ-spouse Craft-repair Husband
## 4905 Never-married Other-service Own-child
## 4906 Married-civ-spouse Exec-managerial Wife
## 4907 Widowed Prof-specialty Not-in-family
## 4908 Never-married Adm-clerical Not-in-family
## 4909 Married-civ-spouse Craft-repair Husband
## 4910 Widowed Prof-specialty Unmarried
## 4911 Married-civ-spouse Machine-op-inspct Husband
## 4912 Never-married Adm-clerical Own-child
## 4913 Never-married Adm-clerical Own-child
## 4914 Never-married Handlers-cleaners Other-relative
## 4915 Married-civ-spouse Craft-repair Husband
## 4916 Never-married Exec-managerial Own-child
## 4917 Married-civ-spouse Other-service Husband
## 4918 Never-married Prof-specialty Not-in-family
## 4919 Married-civ-spouse Exec-managerial Husband
## 4920 Never-married Adm-clerical Other-relative
## 4921 Married-civ-spouse Adm-clerical Wife
## 4922 Married-civ-spouse Exec-managerial Husband
## 4923 Never-married Adm-clerical Not-in-family
## 4924 Married-civ-spouse Sales Husband
## 4925 Divorced Exec-managerial Unmarried
## 4926 Never-married ? Not-in-family
## 4927 Separated Tech-support Unmarried
## 4928 Married-spouse-absent Adm-clerical Own-child
## 4929 Divorced Prof-specialty Not-in-family
## 4930 Divorced Transport-moving Unmarried
## 4931 Never-married Other-service Own-child
## 4932 Never-married Other-service Own-child
## 4933 Divorced Craft-repair Own-child
## 4934 Separated Other-service Unmarried
## 4935 Married-civ-spouse Handlers-cleaners Husband
## 4936 Married-civ-spouse Adm-clerical Husband
## 4937 Widowed Adm-clerical Not-in-family
## 4938 Never-married Adm-clerical Own-child
## 4939 Married-civ-spouse Adm-clerical Wife
## 4940 Married-civ-spouse Adm-clerical Own-child
## 4941 Married-civ-spouse Machine-op-inspct Husband
## 4942 Married-civ-spouse Craft-repair Husband
## 4943 Never-married ? Own-child
## 4944 Married-civ-spouse Exec-managerial Husband
## 4945 Married-civ-spouse Sales Husband
## 4946 Married-civ-spouse Prof-specialty Husband
## 4947 Never-married Sales Not-in-family
## 4948 Married-civ-spouse Machine-op-inspct Husband
## 4949 Divorced Prof-specialty Not-in-family
## 4950 Married-civ-spouse Sales Husband
## 4951 Married-civ-spouse Exec-managerial Husband
## 4952 Married-civ-spouse Sales Husband
## 4953 Married-civ-spouse Exec-managerial Husband
## 4954 Married-civ-spouse Adm-clerical Husband
## 4955 Divorced Sales Not-in-family
## 4956 Married-civ-spouse Other-service Husband
## 4957 Widowed Exec-managerial Unmarried
## 4958 Separated Other-service Own-child
## 4959 Never-married ? Own-child
## 4960 Widowed Adm-clerical Not-in-family
## 4961 Never-married Prof-specialty Own-child
## 4962 Married-civ-spouse Exec-managerial Wife
## 4963 Never-married Craft-repair Own-child
## 4964 Never-married Prof-specialty Not-in-family
## 4965 Married-civ-spouse Transport-moving Husband
## 4966 Never-married Prof-specialty Not-in-family
## 4967 Never-married Machine-op-inspct Own-child
## 4968 Married-civ-spouse Exec-managerial Wife
## 4969 Never-married Exec-managerial Other-relative
## 4970 Never-married Prof-specialty Own-child
## 4971 Married-civ-spouse ? Husband
## 4972 Married-civ-spouse Transport-moving Husband
## 4973 Married-civ-spouse Prof-specialty Husband
## 4974 Married-civ-spouse Farming-fishing Husband
## 4975 Married-civ-spouse Prof-specialty Husband
## 4976 Married-civ-spouse Adm-clerical Husband
## 4977 Married-civ-spouse Craft-repair Husband
## 4978 Married-civ-spouse Craft-repair Husband
## 4979 Divorced Sales Not-in-family
## 4980 Married-spouse-absent Handlers-cleaners Not-in-family
## 4981 Separated ? Own-child
## 4982 Never-married Adm-clerical Not-in-family
## 4983 Never-married ? Own-child
## 4984 Married-civ-spouse Prof-specialty Husband
## 4985 Never-married Craft-repair Own-child
## 4986 Never-married Handlers-cleaners Unmarried
## 4987 Never-married Other-service Own-child
## 4988 Married-civ-spouse Prof-specialty Husband
## 4989 Married-civ-spouse Exec-managerial Husband
## 4990 Separated Exec-managerial Unmarried
## 4991 Divorced Sales Not-in-family
## 4992 Married-civ-spouse Transport-moving Husband
## 4993 Married-civ-spouse Handlers-cleaners Husband
## 4994 Never-married Exec-managerial Not-in-family
## 4995 Married-civ-spouse Exec-managerial Husband
## 4996 Never-married Machine-op-inspct Unmarried
## 4997 Married-civ-spouse Craft-repair Husband
## 4998 Married-civ-spouse Craft-repair Husband
## 4999 Never-married Adm-clerical Not-in-family
## 5000 Never-married Prof-specialty Not-in-family
## 5001 Divorced Prof-specialty Not-in-family
## 5002 Never-married Tech-support Other-relative
## 5003 Married-civ-spouse Exec-managerial Wife
## 5004 Never-married Machine-op-inspct Unmarried
## 5005 Married-civ-spouse Craft-repair Husband
## 5006 Married-civ-spouse Sales Husband
## 5007 Never-married Other-service Not-in-family
## 5008 Married-civ-spouse Sales Husband
## 5009 Married-civ-spouse Transport-moving Husband
## 5010 Married-civ-spouse Protective-serv Husband
## 5011 Never-married Tech-support Not-in-family
## 5012 Never-married Transport-moving Not-in-family
## 5013 Never-married Prof-specialty Not-in-family
## 5014 Married-civ-spouse Prof-specialty Husband
## 5015 Divorced Transport-moving Not-in-family
## 5016 Married-civ-spouse Machine-op-inspct Husband
## 5017 Divorced Prof-specialty Unmarried
## 5018 Divorced Adm-clerical Own-child
## 5019 Never-married Exec-managerial Not-in-family
## 5020 Never-married Handlers-cleaners Not-in-family
## 5021 Never-married ? Own-child
## 5022 Married-civ-spouse Craft-repair Husband
## 5023 Married-civ-spouse Transport-moving Husband
## 5024 Never-married Adm-clerical Not-in-family
## 5025 Married-civ-spouse Adm-clerical Husband
## 5026 Married-civ-spouse Sales Husband
## 5027 Married-civ-spouse Prof-specialty Husband
## 5028 Married-civ-spouse Exec-managerial Wife
## 5029 Married-civ-spouse Farming-fishing Husband
## 5030 Never-married Sales Own-child
## 5031 Never-married Other-service Own-child
## 5032 Married-civ-spouse Sales Husband
## 5033 Never-married Handlers-cleaners Not-in-family
## 5034 Married-civ-spouse Prof-specialty Husband
## 5035 Married-civ-spouse Farming-fishing Husband
## 5036 Married-civ-spouse Priv-house-serv Wife
## 5037 Married-civ-spouse Adm-clerical Wife
## 5038 Married-civ-spouse Exec-managerial Husband
## 5039 Married-civ-spouse Machine-op-inspct Husband
## 5040 Married-civ-spouse Craft-repair Husband
## 5041 Never-married Protective-serv Not-in-family
## 5042 Married-civ-spouse Craft-repair Husband
## 5043 Divorced Adm-clerical Not-in-family
## 5044 Divorced Prof-specialty Not-in-family
## 5045 Married-civ-spouse Exec-managerial Husband
## 5046 Married-civ-spouse Prof-specialty Husband
## 5047 Married-civ-spouse Craft-repair Husband
## 5048 Never-married Transport-moving Not-in-family
## 5049 Separated Farming-fishing Not-in-family
## 5050 Married-civ-spouse Prof-specialty Husband
## 5051 Married-civ-spouse Exec-managerial Husband
## 5052 Married-civ-spouse Sales Husband
## 5053 Separated Sales Not-in-family
## 5054 Married-civ-spouse Prof-specialty Husband
## 5055 Never-married Craft-repair Own-child
## 5056 Never-married Other-service Own-child
## 5057 Married-civ-spouse Machine-op-inspct Husband
## 5058 Married-civ-spouse Other-service Husband
## 5059 Married-civ-spouse Exec-managerial Husband
## 5060 Divorced Adm-clerical Not-in-family
## 5061 Married-civ-spouse Transport-moving Husband
## 5062 Never-married Craft-repair Own-child
## 5063 Married-civ-spouse Craft-repair Husband
## 5064 Married-civ-spouse Craft-repair Husband
## 5065 Never-married ? Own-child
## 5066 Married-spouse-absent Craft-repair Not-in-family
## 5067 Married-civ-spouse Exec-managerial Husband
## 5068 Married-civ-spouse Handlers-cleaners Own-child
## 5069 Never-married Craft-repair Not-in-family
## 5070 Never-married Prof-specialty Not-in-family
## 5071 Never-married Prof-specialty Own-child
## 5072 Married-civ-spouse Farming-fishing Husband
## 5073 Married-civ-spouse Sales Husband
## 5074 Married-civ-spouse Prof-specialty Husband
## 5075 Married-civ-spouse Sales Husband
## 5076 Married-civ-spouse Sales Wife
## 5077 Never-married Other-service Own-child
## 5078 Divorced Other-service Not-in-family
## 5079 Married-civ-spouse Craft-repair Husband
## 5080 Married-civ-spouse Machine-op-inspct Husband
## 5081 Separated Sales Unmarried
## 5082 Never-married Transport-moving Other-relative
## 5083 Divorced Protective-serv Not-in-family
## 5084 Never-married Adm-clerical Not-in-family
## 5085 Never-married Sales Not-in-family
## 5086 Married-civ-spouse Farming-fishing Husband
## 5087 Married-civ-spouse Craft-repair Husband
## 5088 Never-married Craft-repair Own-child
## 5089 Married-civ-spouse Craft-repair Husband
## 5090 Married-civ-spouse Sales Husband
## 5091 Never-married Other-service Own-child
## 5092 Divorced Exec-managerial Unmarried
## 5093 Divorced Craft-repair Own-child
## 5094 Married-civ-spouse Machine-op-inspct Husband
## 5095 Divorced Machine-op-inspct Not-in-family
## 5096 Married-civ-spouse Adm-clerical Wife
## 5097 Widowed Other-service Unmarried
## 5098 Married-civ-spouse Sales Husband
## 5099 Married-civ-spouse Prof-specialty Husband
## 5100 Married-civ-spouse Protective-serv Husband
## 5101 Married-civ-spouse Farming-fishing Husband
## 5102 Married-civ-spouse Exec-managerial Husband
## 5103 Married-civ-spouse Other-service Husband
## 5104 Married-civ-spouse Sales Husband
## 5105 Never-married Other-service Not-in-family
## 5106 Married-spouse-absent Craft-repair Not-in-family
## 5107 Married-civ-spouse Exec-managerial Husband
## 5108 Never-married Craft-repair Own-child
## 5109 Married-civ-spouse Sales Husband
## 5110 Married-spouse-absent Other-service Not-in-family
## 5111 Never-married Machine-op-inspct Own-child
## 5112 Never-married Other-service Unmarried
## 5113 Married-civ-spouse Transport-moving Husband
## 5114 Widowed Machine-op-inspct Unmarried
## 5115 Married-civ-spouse Exec-managerial Husband
## 5116 Never-married Prof-specialty Other-relative
## 5117 Married-civ-spouse Craft-repair Husband
## 5118 Never-married Sales Not-in-family
## 5119 Married-civ-spouse Sales Husband
## 5120 Divorced Other-service Unmarried
## 5121 Married-civ-spouse Prof-specialty Husband
## 5122 Married-civ-spouse Craft-repair Husband
## 5123 Never-married Other-service Not-in-family
## 5124 Married-civ-spouse Prof-specialty Wife
## 5125 Never-married Sales Own-child
## 5126 Never-married Sales Not-in-family
## 5127 Never-married Machine-op-inspct Other-relative
## 5128 Widowed Exec-managerial Not-in-family
## 5129 Never-married Sales Own-child
## 5130 Married-civ-spouse Exec-managerial Husband
## 5131 Divorced Craft-repair Not-in-family
## 5132 Married-civ-spouse Adm-clerical Wife
## 5133 Never-married Exec-managerial Not-in-family
## 5134 Widowed Transport-moving Not-in-family
## 5135 Married-civ-spouse Other-service Husband
## 5136 Widowed Craft-repair Unmarried
## 5137 Never-married Handlers-cleaners Own-child
## 5138 Married-civ-spouse Adm-clerical Wife
## 5139 Never-married Protective-serv Own-child
## 5140 Married-civ-spouse Other-service Husband
## 5141 Married-civ-spouse Exec-managerial Husband
## 5142 Never-married Transport-moving Own-child
## 5143 Never-married Other-service Not-in-family
## 5144 Divorced Adm-clerical Other-relative
## 5145 Married-civ-spouse Sales Husband
## 5146 Separated Sales Unmarried
## 5147 Never-married Exec-managerial Not-in-family
## 5148 Never-married ? Own-child
## 5149 Married-civ-spouse Craft-repair Husband
## 5150 Married-civ-spouse Machine-op-inspct Husband
## 5151 Married-civ-spouse Machine-op-inspct Husband
## 5152 Divorced Sales Unmarried
## 5153 Never-married Adm-clerical Not-in-family
## 5154 Divorced Adm-clerical Unmarried
## 5155 Married-civ-spouse Craft-repair Husband
## 5156 Divorced Farming-fishing Unmarried
## 5157 Widowed ? Not-in-family
## 5158 Married-civ-spouse Adm-clerical Wife
## 5159 Married-civ-spouse Exec-managerial Husband
## 5160 Married-civ-spouse Craft-repair Husband
## 5161 Married-civ-spouse Prof-specialty Wife
## 5162 Married-civ-spouse Adm-clerical Wife
## 5163 Married-civ-spouse Other-service Husband
## 5164 Never-married Sales Own-child
## 5165 Married-civ-spouse Craft-repair Husband
## 5166 Never-married Prof-specialty Not-in-family
## 5167 Married-civ-spouse Adm-clerical Husband
## 5168 Married-civ-spouse Handlers-cleaners Husband
## 5169 Married-civ-spouse Farming-fishing Husband
## 5170 Married-civ-spouse Farming-fishing Husband
## 5171 Married-civ-spouse Craft-repair Husband
## 5172 Never-married Sales Not-in-family
## 5173 Married-civ-spouse ? Wife
## 5174 Married-civ-spouse Exec-managerial Husband
## 5175 Never-married Sales Own-child
## 5176 Married-civ-spouse Exec-managerial Husband
## 5177 Married-civ-spouse Sales Own-child
## 5178 Divorced Adm-clerical Not-in-family
## 5179 Divorced Transport-moving Not-in-family
## 5180 Married-civ-spouse Prof-specialty Wife
## 5181 Divorced Prof-specialty Unmarried
## 5182 Never-married Craft-repair Own-child
## 5183 Married-civ-spouse Handlers-cleaners Own-child
## 5184 Divorced Prof-specialty Not-in-family
## 5185 Married-civ-spouse Exec-managerial Wife
## 5186 Never-married Sales Own-child
## 5187 Married-civ-spouse Sales Husband
## 5188 Widowed Prof-specialty Not-in-family
## 5189 Never-married Transport-moving Not-in-family
## 5190 Married-civ-spouse Adm-clerical Wife
## 5191 Married-civ-spouse Craft-repair Husband
## 5192 Never-married Other-service Own-child
## 5193 Married-civ-spouse Exec-managerial Husband
## 5194 Widowed ? Not-in-family
## 5195 Married-civ-spouse Machine-op-inspct Husband
## 5196 Divorced Adm-clerical Unmarried
## 5197 Married-civ-spouse Prof-specialty Husband
## 5198 Married-civ-spouse Sales Husband
## 5199 Married-civ-spouse ? Husband
## 5200 Divorced Adm-clerical Unmarried
## 5201 Never-married Exec-managerial Not-in-family
## 5202 Married-civ-spouse Prof-specialty Husband
## 5203 Married-civ-spouse Prof-specialty Husband
## 5204 Never-married Prof-specialty Own-child
## 5205 Never-married Craft-repair Not-in-family
## 5206 Never-married Prof-specialty Not-in-family
## 5207 Never-married Other-service Not-in-family
## 5208 Divorced ? Other-relative
## 5209 Never-married Other-service Unmarried
## 5210 Never-married ? Own-child
## 5211 Never-married Sales Other-relative
## 5212 Married-civ-spouse Machine-op-inspct Husband
## 5213 Never-married Farming-fishing Not-in-family
## 5214 Never-married Sales Other-relative
## 5215 Widowed ? Not-in-family
## 5216 Married-civ-spouse Exec-managerial Husband
## 5217 Never-married Sales Not-in-family
## 5218 Never-married Craft-repair Not-in-family
## 5219 Never-married Exec-managerial Not-in-family
## 5220 Married-civ-spouse Exec-managerial Wife
## 5221 Married-civ-spouse Prof-specialty Husband
## 5222 Never-married Adm-clerical Own-child
## 5223 Married-civ-spouse Prof-specialty Husband
## 5224 Divorced Adm-clerical Not-in-family
## 5225 Married-civ-spouse Exec-managerial Husband
## 5226 Married-civ-spouse Machine-op-inspct Husband
## 5227 Married-civ-spouse Craft-repair Husband
## 5228 Married-civ-spouse Exec-managerial Wife
## 5229 Never-married ? Not-in-family
## 5230 Married-civ-spouse Handlers-cleaners Husband
## 5231 Married-civ-spouse Other-service Husband
## 5232 Divorced Exec-managerial Not-in-family
## 5233 Divorced Other-service Not-in-family
## 5234 Married-civ-spouse Sales Husband
## 5235 Married-civ-spouse Adm-clerical Wife
## 5236 Never-married Sales Not-in-family
## 5237 Married-civ-spouse Tech-support Husband
## 5238 Never-married Sales Own-child
## 5239 Married-civ-spouse Protective-serv Husband
## 5240 Never-married Prof-specialty Own-child
## 5241 Never-married Tech-support Own-child
## 5242 Married-spouse-absent Other-service Not-in-family
## 5243 Married-civ-spouse Exec-managerial Husband
## 5244 Separated Prof-specialty Unmarried
## 5245 Married-civ-spouse Craft-repair Husband
## 5246 Divorced Exec-managerial Not-in-family
## 5247 Divorced Adm-clerical Not-in-family
## 5248 Never-married Handlers-cleaners Own-child
## 5249 Married-civ-spouse Craft-repair Husband
## 5250 Married-civ-spouse Sales Husband
## 5251 Married-civ-spouse Sales Husband
## 5252 Married-civ-spouse Prof-specialty Wife
## 5253 Never-married Machine-op-inspct Own-child
## 5254 Never-married Protective-serv Not-in-family
## 5255 Married-civ-spouse Machine-op-inspct Husband
## 5256 Divorced ? Unmarried
## 5257 Never-married Other-service Own-child
## 5258 Never-married Craft-repair Not-in-family
## 5259 Never-married Adm-clerical Own-child
## 5260 Never-married Other-service Own-child
## 5261 Never-married Adm-clerical Own-child
## 5262 Never-married Craft-repair Not-in-family
## 5263 Divorced Tech-support Not-in-family
## 5264 Never-married Other-service Own-child
## 5265 Married-civ-spouse Sales Husband
## 5266 Never-married Adm-clerical Not-in-family
## 5267 Never-married Transport-moving Own-child
## 5268 Married-civ-spouse Sales Husband
## 5269 Never-married Prof-specialty Own-child
## 5270 Married-civ-spouse Transport-moving Husband
## 5271 Divorced Transport-moving Not-in-family
## 5272 Never-married Transport-moving Other-relative
## 5273 Never-married Adm-clerical Not-in-family
## 5274 Married-civ-spouse Sales Husband
## 5275 Married-civ-spouse Protective-serv Husband
## 5276 Married-civ-spouse Prof-specialty Husband
## 5277 Married-civ-spouse Exec-managerial Husband
## 5278 Never-married Prof-specialty Own-child
## 5279 Married-civ-spouse Handlers-cleaners Husband
## 5280 Divorced Sales Not-in-family
## 5281 Married-civ-spouse Protective-serv Husband
## 5282 Never-married Handlers-cleaners Not-in-family
## 5283 Married-civ-spouse Exec-managerial Husband
## 5284 Divorced Prof-specialty Not-in-family
## 5285 Never-married Sales Own-child
## 5286 Married-civ-spouse Adm-clerical Wife
## 5287 Separated Handlers-cleaners Other-relative
## 5288 Married-civ-spouse Exec-managerial Wife
## 5289 Never-married Transport-moving Not-in-family
## 5290 Married-civ-spouse Machine-op-inspct Husband
## 5291 Married-civ-spouse Prof-specialty Husband
## 5292 Widowed Prof-specialty Not-in-family
## 5293 Never-married Sales Own-child
## 5294 Married-civ-spouse Farming-fishing Husband
## 5295 Divorced ? Not-in-family
## 5296 Married-civ-spouse Transport-moving Husband
## 5297 Married-civ-spouse ? Husband
## 5298 Married-civ-spouse Handlers-cleaners Husband
## 5299 Married-civ-spouse Adm-clerical Wife
## 5300 Married-civ-spouse Farming-fishing Husband
## 5301 Divorced Handlers-cleaners Unmarried
## 5302 Married-civ-spouse Machine-op-inspct Husband
## 5303 Never-married ? Own-child
## 5304 Never-married Other-service Own-child
## 5305 Never-married Exec-managerial Own-child
## 5306 Divorced Adm-clerical Not-in-family
## 5307 Married-civ-spouse Farming-fishing Husband
## 5308 Separated ? Unmarried
## 5309 Never-married Other-service Own-child
## 5310 Never-married Craft-repair Unmarried
## 5311 Divorced Exec-managerial Not-in-family
## 5312 Never-married Prof-specialty Not-in-family
## 5313 Married-civ-spouse Exec-managerial Husband
## 5314 Never-married Adm-clerical Not-in-family
## 5315 Married-civ-spouse Farming-fishing Husband
## 5316 Never-married Craft-repair Own-child
## 5317 Never-married Adm-clerical Own-child
## 5318 Married-civ-spouse Craft-repair Husband
## 5319 Married-civ-spouse Protective-serv Husband
## 5320 Married-civ-spouse Craft-repair Husband
## 5321 Never-married ? Not-in-family
## 5322 Never-married ? Own-child
## 5323 Never-married Sales Not-in-family
## 5324 Married-civ-spouse Transport-moving Husband
## 5325 Divorced Machine-op-inspct Not-in-family
## 5326 Never-married Handlers-cleaners Own-child
## 5327 Married-civ-spouse Farming-fishing Husband
## 5328 Married-civ-spouse Protective-serv Husband
## 5329 Never-married Exec-managerial Own-child
## 5330 Married-civ-spouse Prof-specialty Husband
## 5331 Married-civ-spouse Other-service Husband
## 5332 Never-married Exec-managerial Own-child
## 5333 Never-married Handlers-cleaners Own-child
## 5334 Divorced Handlers-cleaners Other-relative
## 5335 Married-civ-spouse Exec-managerial Husband
## 5336 Divorced Exec-managerial Not-in-family
## 5337 Never-married Craft-repair Not-in-family
## 5338 Divorced Prof-specialty Unmarried
## 5339 Divorced Adm-clerical Not-in-family
## 5340 Never-married Exec-managerial Not-in-family
## 5341 Married-civ-spouse ? Husband
## 5342 Never-married Machine-op-inspct Own-child
## 5343 Married-civ-spouse Machine-op-inspct Husband
## 5344 Divorced Adm-clerical Unmarried
## 5345 Married-spouse-absent ? Not-in-family
## 5346 Married-civ-spouse Handlers-cleaners Husband
## 5347 Never-married ? Own-child
## 5348 Married-civ-spouse Machine-op-inspct Husband
## 5349 Never-married Other-service Own-child
## 5350 Married-civ-spouse Exec-managerial Wife
## 5351 Never-married Other-service Own-child
## 5352 Never-married Prof-specialty Not-in-family
## 5353 Never-married Craft-repair Own-child
## 5354 Married-civ-spouse Transport-moving Husband
## 5355 Divorced Exec-managerial Unmarried
## 5356 Married-civ-spouse Handlers-cleaners Husband
## 5357 Never-married Sales Own-child
## 5358 Married-civ-spouse Handlers-cleaners Husband
## 5359 Married-civ-spouse Prof-specialty Husband
## 5360 Never-married Other-service Not-in-family
## 5361 Never-married Craft-repair Own-child
## 5362 Never-married ? Own-child
## 5363 Married-civ-spouse Craft-repair Husband
## 5364 Never-married Craft-repair Own-child
## 5365 Married-civ-spouse Sales Husband
## 5366 Married-civ-spouse Other-service Husband
## 5367 Never-married Prof-specialty Own-child
## 5368 Never-married Sales Own-child
## 5369 Married-civ-spouse Sales Husband
## 5370 Married-civ-spouse Transport-moving Husband
## 5371 Married-civ-spouse Exec-managerial Husband
## 5372 Never-married Protective-serv Other-relative
## 5373 Married-civ-spouse Sales Husband
## 5374 Married-civ-spouse Craft-repair Husband
## 5375 Married-civ-spouse Protective-serv Husband
## 5376 Never-married Exec-managerial Not-in-family
## 5377 Married-civ-spouse Craft-repair Husband
## 5378 Separated Prof-specialty Not-in-family
## 5379 Married-civ-spouse Transport-moving Husband
## 5380 Married-civ-spouse Sales Husband
## 5381 Married-civ-spouse Exec-managerial Wife
## 5382 Never-married Adm-clerical Not-in-family
## 5383 Married-civ-spouse Sales Husband
## 5384 Never-married ? Own-child
## 5385 Never-married ? Not-in-family
## 5386 Divorced Handlers-cleaners Not-in-family
## 5387 Married-civ-spouse Exec-managerial Husband
## 5388 Married-civ-spouse Adm-clerical Wife
## 5389 Divorced Tech-support Unmarried
## 5390 Married-civ-spouse Sales Husband
## 5391 Divorced Other-service Unmarried
## 5392 Married-civ-spouse Machine-op-inspct Wife
## 5393 Widowed Adm-clerical Not-in-family
## 5394 Never-married Tech-support Not-in-family
## 5395 Married-civ-spouse Prof-specialty Husband
## 5396 Never-married Adm-clerical Not-in-family
## 5397 Never-married Other-service Own-child
## 5398 Never-married Adm-clerical Own-child
## 5399 Divorced Craft-repair Not-in-family
## 5400 Divorced Other-service Unmarried
## 5401 Married-civ-spouse Exec-managerial Husband
## 5402 Never-married Transport-moving Other-relative
## 5403 Married-spouse-absent Prof-specialty Not-in-family
## 5404 Married-civ-spouse Exec-managerial Husband
## 5405 Married-civ-spouse Craft-repair Husband
## 5406 Married-civ-spouse Prof-specialty Husband
## 5407 Never-married Exec-managerial Not-in-family
## 5408 Divorced Prof-specialty Unmarried
## 5409 Married-civ-spouse Sales Husband
## 5410 Divorced Sales Not-in-family
## 5411 Married-civ-spouse Adm-clerical Other-relative
## 5412 Widowed Prof-specialty Not-in-family
## 5413 Never-married Other-service Own-child
## 5414 Divorced Adm-clerical Not-in-family
## 5415 Married-civ-spouse Sales Husband
## 5416 Never-married Adm-clerical Own-child
## 5417 Married-civ-spouse Other-service Husband
## 5418 Married-civ-spouse Sales Husband
## 5419 Married-civ-spouse Craft-repair Husband
## 5420 Married-civ-spouse Transport-moving Husband
## 5421 Married-civ-spouse Farming-fishing Husband
## 5422 Married-civ-spouse Farming-fishing Husband
## 5423 Never-married Prof-specialty Not-in-family
## 5424 Divorced Other-service Unmarried
## 5425 Never-married Other-service Not-in-family
## 5426 Married-civ-spouse Exec-managerial Husband
## 5427 Never-married Handlers-cleaners Other-relative
## 5428 Married-civ-spouse Adm-clerical Husband
## 5429 Married-civ-spouse Machine-op-inspct Husband
## 5430 Divorced Sales Not-in-family
## 5431 Married-civ-spouse Craft-repair Husband
## 5432 Married-civ-spouse Other-service Wife
## 5433 Divorced Prof-specialty Not-in-family
## 5434 Divorced Adm-clerical Not-in-family
## 5435 Never-married Adm-clerical Own-child
## 5436 Married-spouse-absent Machine-op-inspct Not-in-family
## 5437 Never-married Prof-specialty Not-in-family
## 5438 Married-civ-spouse Exec-managerial Husband
## 5439 Married-civ-spouse Craft-repair Husband
## 5440 Divorced Adm-clerical Own-child
## 5441 Never-married ? Not-in-family
## 5442 Married-civ-spouse Craft-repair Wife
## 5443 Married-civ-spouse Craft-repair Wife
## 5444 Married-civ-spouse Exec-managerial Husband
## 5445 Married-civ-spouse Sales Husband
## 5446 Divorced Machine-op-inspct Own-child
## 5447 Never-married ? Own-child
## 5448 Never-married Other-service Own-child
## 5449 Never-married Transport-moving Own-child
## 5450 Never-married Handlers-cleaners Own-child
## 5451 Never-married Sales Own-child
## 5452 Separated Machine-op-inspct Not-in-family
## 5453 Never-married Adm-clerical Own-child
## 5454 Divorced Craft-repair Unmarried
## 5455 Married-civ-spouse Adm-clerical Wife
## 5456 Married-civ-spouse Craft-repair Husband
## 5457 Married-civ-spouse Prof-specialty Husband
## 5458 Married-civ-spouse Adm-clerical Husband
## 5459 Married-civ-spouse Prof-specialty Husband
## 5460 Never-married Exec-managerial Own-child
## 5461 Never-married Adm-clerical Own-child
## 5462 Never-married Sales Not-in-family
## 5463 Married-civ-spouse Sales Husband
## 5464 Married-spouse-absent Machine-op-inspct Not-in-family
## 5465 Divorced Exec-managerial Not-in-family
## 5466 Divorced Exec-managerial Not-in-family
## 5467 Never-married Handlers-cleaners Own-child
## 5468 Never-married Adm-clerical Other-relative
## 5469 Divorced Other-service Unmarried
## 5470 Never-married Prof-specialty Own-child
## 5471 Divorced Craft-repair Not-in-family
## 5472 Widowed ? Not-in-family
## 5473 Never-married Handlers-cleaners Own-child
## 5474 Married-civ-spouse Prof-specialty Husband
## 5475 Married-civ-spouse Other-service Wife
## 5476 Married-civ-spouse Craft-repair Husband
## 5477 Never-married Other-service Own-child
## 5478 Never-married Prof-specialty Own-child
## 5479 Married-civ-spouse Craft-repair Husband
## 5480 Married-civ-spouse Exec-managerial Husband
## 5481 Married-civ-spouse Tech-support Husband
## 5482 Married-spouse-absent Adm-clerical Unmarried
## 5483 Never-married Sales Own-child
## 5484 Never-married Other-service Not-in-family
## 5485 Married-civ-spouse Prof-specialty Husband
## 5486 Divorced Machine-op-inspct Not-in-family
## 5487 Married-civ-spouse Sales Husband
## 5488 Married-civ-spouse Prof-specialty Husband
## 5489 Married-civ-spouse Craft-repair Husband
## 5490 Divorced Exec-managerial Not-in-family
## 5491 Married-civ-spouse Sales Husband
## 5492 Never-married Protective-serv Own-child
## 5493 Divorced ? Not-in-family
## 5494 Never-married Sales Not-in-family
## 5495 Married-civ-spouse Prof-specialty Husband
## 5496 Never-married Adm-clerical Not-in-family
## 5497 Divorced Machine-op-inspct Not-in-family
## 5498 Married-civ-spouse Adm-clerical Husband
## 5499 Divorced Other-service Not-in-family
## 5500 Divorced Craft-repair Unmarried
## 5501 Married-civ-spouse Sales Husband
## 5502 Divorced Prof-specialty Other-relative
## 5503 Never-married Adm-clerical Not-in-family
## 5504 Never-married Machine-op-inspct Unmarried
## 5505 Never-married Adm-clerical Unmarried
## 5506 Married-civ-spouse Craft-repair Husband
## 5507 Never-married Craft-repair Own-child
## 5508 Married-civ-spouse Craft-repair Husband
## 5509 Never-married Sales Own-child
## 5510 Married-civ-spouse Other-service Husband
## 5511 Never-married Other-service Own-child
## 5512 Widowed Machine-op-inspct Not-in-family
## 5513 Never-married Sales Own-child
## 5514 Divorced Sales Not-in-family
## 5515 Never-married Prof-specialty Not-in-family
## 5516 Never-married Adm-clerical Own-child
## 5517 Married-civ-spouse Craft-repair Husband
## 5518 Married-civ-spouse Machine-op-inspct Husband
## 5519 Married-civ-spouse Prof-specialty Husband
## 5520 Never-married Sales Not-in-family
## 5521 Never-married Other-service Not-in-family
## 5522 Married-civ-spouse Transport-moving Husband
## 5523 Never-married Craft-repair Unmarried
## 5524 Married-civ-spouse Adm-clerical Wife
## 5525 Never-married Craft-repair Other-relative
## 5526 Never-married Exec-managerial Not-in-family
## 5527 Never-married ? Own-child
## 5528 Separated Other-service Unmarried
## 5529 Divorced Prof-specialty Own-child
## 5530 Married-civ-spouse ? Husband
## 5531 Married-civ-spouse Prof-specialty Wife
## 5532 Married-civ-spouse Machine-op-inspct Husband
## 5533 Divorced Prof-specialty Not-in-family
## 5534 Divorced Sales Unmarried
## 5535 Separated Other-service Unmarried
## 5536 Never-married Other-service Own-child
## 5537 Married-civ-spouse Transport-moving Husband
## 5538 Married-civ-spouse Prof-specialty Husband
## 5539 Married-civ-spouse Prof-specialty Husband
## 5540 Never-married Exec-managerial Not-in-family
## 5541 Married-civ-spouse Handlers-cleaners Husband
## 5542 Never-married Machine-op-inspct Not-in-family
## 5543 Married-civ-spouse Exec-managerial Wife
## 5544 Married-civ-spouse Exec-managerial Husband
## 5545 Divorced Protective-serv Not-in-family
## 5546 Never-married Adm-clerical Own-child
## 5547 Never-married Other-service Not-in-family
## 5548 Married-civ-spouse Sales Husband
## 5549 Separated ? Not-in-family
## 5550 Married-civ-spouse Prof-specialty Husband
## 5551 Married-civ-spouse Craft-repair Husband
## 5552 Separated Protective-serv Not-in-family
## 5553 Married-civ-spouse Sales Husband
## 5554 Never-married Sales Own-child
## 5555 Married-civ-spouse Craft-repair Husband
## 5556 Married-civ-spouse Exec-managerial Husband
## 5557 Never-married Sales Own-child
## 5558 Separated Other-service Unmarried
## 5559 Married-civ-spouse Sales Husband
## 5560 Married-spouse-absent Adm-clerical Not-in-family
## 5561 Married-civ-spouse Prof-specialty Husband
## 5562 Widowed ? Not-in-family
## 5563 Married-civ-spouse Protective-serv Husband
## 5564 Divorced Craft-repair Not-in-family
## 5565 Married-civ-spouse Tech-support Husband
## 5566 Never-married ? Other-relative
## 5567 Married-civ-spouse Sales Husband
## 5568 Separated Transport-moving Not-in-family
## 5569 Married-civ-spouse Craft-repair Husband
## 5570 Never-married Exec-managerial Not-in-family
## 5571 Married-civ-spouse Craft-repair Husband
## 5572 Divorced Adm-clerical Other-relative
## 5573 Divorced Sales Not-in-family
## 5574 Married-civ-spouse Transport-moving Husband
## 5575 Never-married Exec-managerial Not-in-family
## 5576 Married-civ-spouse Farming-fishing Husband
## 5577 Never-married Transport-moving Not-in-family
## 5578 Married-civ-spouse Machine-op-inspct Husband
## 5579 Never-married Priv-house-serv Not-in-family
## 5580 Never-married Machine-op-inspct Not-in-family
## 5581 Married-civ-spouse Tech-support Husband
## 5582 Married-civ-spouse Craft-repair Husband
## 5583 Married-civ-spouse Exec-managerial Husband
## 5584 Never-married Craft-repair Own-child
## 5585 Married-civ-spouse Exec-managerial Husband
## 5586 Divorced Adm-clerical Unmarried
## 5587 Married-civ-spouse Prof-specialty Husband
## 5588 Married-civ-spouse Handlers-cleaners Husband
## 5589 Married-civ-spouse Prof-specialty Husband
## 5590 Married-civ-spouse Exec-managerial Husband
## 5591 Widowed ? Not-in-family
## 5592 Never-married Sales Not-in-family
## 5593 Never-married Sales Other-relative
## 5594 Married-civ-spouse Prof-specialty Husband
## 5595 Never-married Other-service Other-relative
## 5596 Married-civ-spouse Craft-repair Husband
## 5597 Married-civ-spouse Prof-specialty Husband
## 5598 Never-married Craft-repair Other-relative
## 5599 Never-married Handlers-cleaners Own-child
## 5600 Never-married Sales Not-in-family
## 5601 Married-civ-spouse Tech-support Husband
## 5602 Married-civ-spouse Exec-managerial Husband
## 5603 Married-civ-spouse Prof-specialty Husband
## 5604 Never-married Exec-managerial Own-child
## 5605 Never-married Other-service Other-relative
## 5606 Separated Prof-specialty Unmarried
## 5607 Married-civ-spouse Exec-managerial Husband
## 5608 Never-married Craft-repair Not-in-family
## 5609 Divorced Transport-moving Unmarried
## 5610 Divorced Sales Unmarried
## 5611 Married-civ-spouse Protective-serv Husband
## 5612 Married-civ-spouse Sales Own-child
## 5613 Married-civ-spouse Adm-clerical Husband
## 5614 Never-married Sales Own-child
## 5615 Never-married Craft-repair Not-in-family
## 5616 Divorced Farming-fishing Not-in-family
## 5617 Never-married Other-service Own-child
## 5618 Never-married Exec-managerial Not-in-family
## 5619 Never-married Exec-managerial Not-in-family
## 5620 Married-civ-spouse Other-service Husband
## 5621 Married-civ-spouse Transport-moving Husband
## 5622 Never-married Craft-repair Own-child
## 5623 Divorced Other-service Unmarried
## 5624 Married-civ-spouse ? Husband
## 5625 Married-civ-spouse Adm-clerical Wife
## 5626 Married-civ-spouse Exec-managerial Husband
## 5627 Married-spouse-absent Other-service Not-in-family
## 5628 Divorced Adm-clerical Unmarried
## 5629 Married-civ-spouse Farming-fishing Husband
## 5630 Married-civ-spouse Craft-repair Other-relative
## 5631 Separated Sales Not-in-family
## 5632 Married-civ-spouse Other-service Husband
## 5633 Married-civ-spouse ? Wife
## 5634 Never-married Adm-clerical Own-child
## 5635 Married-civ-spouse Exec-managerial Husband
## 5636 Divorced Prof-specialty Unmarried
## 5637 Married-spouse-absent Craft-repair Not-in-family
## 5638 Never-married Sales Own-child
## 5639 Never-married Craft-repair Own-child
## 5640 Married-civ-spouse Tech-support Husband
## 5641 Married-civ-spouse Other-service Husband
## 5642 Married-civ-spouse Transport-moving Husband
## 5643 Married-civ-spouse Sales Husband
## 5644 Married-civ-spouse Tech-support Husband
## 5645 Divorced Sales Not-in-family
## 5646 Divorced Other-service Not-in-family
## 5647 Divorced Transport-moving Not-in-family
## 5648 Never-married Adm-clerical Not-in-family
## 5649 Divorced Protective-serv Not-in-family
## 5650 Married-civ-spouse Prof-specialty Husband
## 5651 Never-married Machine-op-inspct Own-child
## 5652 Never-married Craft-repair Not-in-family
## 5653 Married-civ-spouse ? Husband
## 5654 Married-civ-spouse Other-service Husband
## 5655 Never-married Tech-support Not-in-family
## 5656 Never-married Sales Not-in-family
## 5657 Never-married Other-service Own-child
## 5658 Married-civ-spouse Craft-repair Husband
## 5659 Married-civ-spouse Exec-managerial Wife
## 5660 Never-married Prof-specialty Not-in-family
## 5661 Married-civ-spouse Machine-op-inspct Husband
## 5662 Never-married Craft-repair Own-child
## 5663 Married-civ-spouse Exec-managerial Wife
## 5664 Divorced Craft-repair Not-in-family
## 5665 Separated Handlers-cleaners Unmarried
## 5666 Divorced Other-service Not-in-family
## 5667 Married-civ-spouse Transport-moving Husband
## 5668 Never-married Machine-op-inspct Not-in-family
## 5669 Never-married Prof-specialty Not-in-family
## 5670 Married-civ-spouse Craft-repair Husband
## 5671 Never-married Prof-specialty Not-in-family
## 5672 Married-civ-spouse Protective-serv Husband
## 5673 Never-married Sales Not-in-family
## 5674 Married-civ-spouse Adm-clerical Husband
## 5675 Divorced Adm-clerical Own-child
## 5676 Divorced Prof-specialty Not-in-family
## 5677 Never-married Other-service Other-relative
## 5678 Never-married Prof-specialty Not-in-family
## 5679 Married-spouse-absent Adm-clerical Unmarried
## 5680 Married-civ-spouse ? Husband
## 5681 Married-civ-spouse Prof-specialty Husband
## 5682 Married-civ-spouse Sales Husband
## 5683 Married-civ-spouse Exec-managerial Husband
## 5684 Divorced Tech-support Not-in-family
## 5685 Never-married Craft-repair Own-child
## 5686 Married-civ-spouse Sales Husband
## 5687 Widowed Adm-clerical Not-in-family
## 5688 Married-civ-spouse ? Husband
## 5689 Married-civ-spouse Adm-clerical Wife
## 5690 Never-married Other-service Own-child
## 5691 Married-civ-spouse Craft-repair Husband
## 5692 Divorced Exec-managerial Not-in-family
## 5693 Never-married Other-service Own-child
## 5694 Separated Adm-clerical Unmarried
## 5695 Married-civ-spouse Tech-support Husband
## 5696 Divorced Sales Not-in-family
## 5697 Married-civ-spouse Protective-serv Husband
## 5698 Never-married Adm-clerical Unmarried
## 5699 Married-civ-spouse Machine-op-inspct Husband
## 5700 Never-married Sales Not-in-family
## 5701 Married-civ-spouse Machine-op-inspct Husband
## 5702 Married-civ-spouse Craft-repair Husband
## 5703 Married-civ-spouse Craft-repair Husband
## 5704 Married-civ-spouse Prof-specialty Husband
## 5705 Divorced Other-service Unmarried
## 5706 Married-civ-spouse Exec-managerial Husband
## 5707 Never-married Craft-repair Not-in-family
## 5708 Married-civ-spouse Prof-specialty Husband
## 5709 Married-civ-spouse Prof-specialty Husband
## 5710 Married-civ-spouse Prof-specialty Husband
## 5711 Married-civ-spouse Adm-clerical Husband
## 5712 Married-civ-spouse Adm-clerical Husband
## 5713 Married-civ-spouse Craft-repair Husband
## 5714 Never-married Prof-specialty Not-in-family
## 5715 Married-civ-spouse Machine-op-inspct Husband
## 5716 Never-married Other-service Own-child
## 5717 Never-married Other-service Not-in-family
## 5718 Married-civ-spouse Sales Husband
## 5719 Married-civ-spouse Farming-fishing Husband
## 5720 Divorced Machine-op-inspct Not-in-family
## 5721 Never-married ? Own-child
## 5722 Divorced Craft-repair Unmarried
## 5723 Never-married Sales Own-child
## 5724 Never-married Adm-clerical Other-relative
## 5725 Never-married Handlers-cleaners Own-child
## 5726 Married-civ-spouse Exec-managerial Wife
## 5727 Separated Machine-op-inspct Own-child
## 5728 Divorced Transport-moving Not-in-family
## 5729 Married-civ-spouse Protective-serv Husband
## 5730 Divorced Exec-managerial Unmarried
## 5731 Never-married Protective-serv Not-in-family
## 5732 Married-civ-spouse Exec-managerial Own-child
## 5733 Never-married Prof-specialty Own-child
## 5734 Married-civ-spouse Exec-managerial Husband
## 5735 Married-civ-spouse Craft-repair Husband
## 5736 Never-married Handlers-cleaners Own-child
## 5737 Married-civ-spouse Machine-op-inspct Husband
## 5738 Married-civ-spouse Machine-op-inspct Husband
## 5739 Married-civ-spouse Craft-repair Husband
## 5740 Never-married Priv-house-serv Own-child
## 5741 Married-civ-spouse Other-service Husband
## 5742 Never-married Machine-op-inspct Own-child
## 5743 Separated Adm-clerical Unmarried
## 5744 Married-civ-spouse Craft-repair Husband
## 5745 Never-married Prof-specialty Not-in-family
## 5746 Married-civ-spouse Transport-moving Husband
## 5747 Never-married Sales Own-child
## 5748 Married-civ-spouse Prof-specialty Husband
## 5749 Married-civ-spouse Exec-managerial Husband
## 5750 Married-civ-spouse Machine-op-inspct Husband
## 5751 Divorced Exec-managerial Unmarried
## 5752 Married-civ-spouse Machine-op-inspct Husband
## 5753 Married-civ-spouse Machine-op-inspct Husband
## 5754 Married-civ-spouse ? Wife
## 5755 Married-civ-spouse Prof-specialty Husband
## 5756 Married-civ-spouse Handlers-cleaners Husband
## 5757 Married-civ-spouse Transport-moving Husband
## 5758 Married-civ-spouse Prof-specialty Husband
## 5759 Never-married Adm-clerical Own-child
## 5760 Divorced Tech-support Other-relative
## 5761 Never-married Prof-specialty Not-in-family
## 5762 Married-civ-spouse Exec-managerial Husband
## 5763 Married-civ-spouse Tech-support Husband
## 5764 Never-married Other-service Own-child
## 5765 Married-civ-spouse Prof-specialty Husband
## 5766 Married-civ-spouse Transport-moving Wife
## 5767 Married-civ-spouse ? Wife
## 5768 Married-civ-spouse Craft-repair Husband
## 5769 Never-married Adm-clerical Unmarried
## 5770 Married-civ-spouse Transport-moving Husband
## 5771 Never-married Sales Other-relative
## 5772 Married-civ-spouse Tech-support Husband
## 5773 Divorced Other-service Not-in-family
## 5774 Married-civ-spouse Craft-repair Husband
## 5775 Widowed Adm-clerical Not-in-family
## 5776 Divorced Tech-support Not-in-family
## 5777 Married-civ-spouse Craft-repair Husband
## 5778 Married-civ-spouse Adm-clerical Husband
## 5779 Never-married Adm-clerical Other-relative
## 5780 Never-married Prof-specialty Not-in-family
## 5781 Divorced Sales Not-in-family
## 5782 Never-married Adm-clerical Own-child
## 5783 Never-married Tech-support Not-in-family
## 5784 Never-married Tech-support Own-child
## 5785 Married-civ-spouse Other-service Wife
## 5786 Never-married Exec-managerial Own-child
## 5787 Married-civ-spouse Craft-repair Husband
## 5788 Divorced Adm-clerical Not-in-family
## 5789 Divorced ? Own-child
## 5790 Divorced Other-service Unmarried
## 5791 Married-civ-spouse Machine-op-inspct Husband
## 5792 Married-civ-spouse Craft-repair Husband
## 5793 Married-civ-spouse Exec-managerial Husband
## 5794 Married-civ-spouse Exec-managerial Husband
## 5795 Married-civ-spouse Protective-serv Husband
## 5796 Married-civ-spouse Transport-moving Husband
## 5797 Married-civ-spouse Adm-clerical Husband
## 5798 Never-married Adm-clerical Own-child
## 5799 Married-civ-spouse Exec-managerial Husband
## 5800 Never-married Craft-repair Own-child
## 5801 Divorced Other-service Not-in-family
## 5802 Never-married Craft-repair Not-in-family
## 5803 Divorced Prof-specialty Unmarried
## 5804 Divorced ? Own-child
## 5805 Never-married Farming-fishing Own-child
## 5806 Never-married Tech-support Not-in-family
## 5807 Married-civ-spouse Transport-moving Husband
## 5808 Never-married Other-service Own-child
## 5809 Married-civ-spouse ? Husband
## 5810 Divorced Sales Unmarried
## 5811 Married-civ-spouse Machine-op-inspct Husband
## 5812 Married-civ-spouse Exec-managerial Wife
## 5813 Divorced Transport-moving Not-in-family
## 5814 Married-civ-spouse Exec-managerial Husband
## 5815 Separated Craft-repair Not-in-family
## 5816 Married-civ-spouse Exec-managerial Husband
## 5817 Married-civ-spouse Sales Husband
## 5818 Married-civ-spouse Sales Husband
## 5819 Never-married Other-service Not-in-family
## 5820 Widowed Other-service Unmarried
## 5821 Married-civ-spouse Exec-managerial Husband
## 5822 Married-civ-spouse Machine-op-inspct Husband
## 5823 Married-civ-spouse Prof-specialty Wife
## 5824 Married-civ-spouse Machine-op-inspct Husband
## 5825 Married-civ-spouse Sales Husband
## 5826 Divorced Farming-fishing Unmarried
## 5827 Never-married Handlers-cleaners Not-in-family
## 5828 Married-civ-spouse Craft-repair Husband
## 5829 Never-married Sales Own-child
## 5830 Never-married Other-service Own-child
## 5831 Widowed Machine-op-inspct Not-in-family
## 5832 Married-civ-spouse Exec-managerial Husband
## 5833 Widowed ? Not-in-family
## 5834 Never-married Exec-managerial Own-child
## 5835 Married-civ-spouse Other-service Husband
## 5836 Divorced Exec-managerial Unmarried
## 5837 Married-civ-spouse Prof-specialty Husband
## 5838 Divorced Exec-managerial Not-in-family
## 5839 Never-married Adm-clerical Not-in-family
## 5840 Married-civ-spouse Exec-managerial Husband
## 5841 Never-married Transport-moving Not-in-family
## 5842 Married-spouse-absent Prof-specialty Other-relative
## 5843 Never-married Priv-house-serv Not-in-family
## 5844 Never-married Other-service Own-child
## 5845 Never-married Handlers-cleaners Own-child
## 5846 Married-civ-spouse Transport-moving Husband
## 5847 Never-married Other-service Other-relative
## 5848 Divorced Craft-repair Own-child
## 5849 Married-civ-spouse Exec-managerial Husband
## 5850 Never-married Other-service Own-child
## 5851 Never-married Other-service Own-child
## 5852 Never-married Adm-clerical Own-child
## 5853 Never-married Machine-op-inspct Own-child
## 5854 Separated ? Unmarried
## 5855 Married-civ-spouse Craft-repair Husband
## 5856 Married-civ-spouse Farming-fishing Husband
## 5857 Married-civ-spouse Craft-repair Husband
## 5858 Married-civ-spouse Craft-repair Husband
## 5859 Never-married Machine-op-inspct Not-in-family
## 5860 Never-married Adm-clerical Own-child
## 5861 Married-civ-spouse Handlers-cleaners Husband
## 5862 Married-civ-spouse Sales Husband
## 5863 Married-civ-spouse Sales Husband
## 5864 Never-married Prof-specialty Not-in-family
## 5865 Divorced Transport-moving Not-in-family
## 5866 Never-married Prof-specialty Not-in-family
## 5867 Married-civ-spouse Farming-fishing Husband
## 5868 Married-civ-spouse Craft-repair Husband
## 5869 Separated Sales Own-child
## 5870 Married-civ-spouse Machine-op-inspct Husband
## 5871 Married-spouse-absent Craft-repair Not-in-family
## 5872 Married-civ-spouse Prof-specialty Husband
## 5873 Never-married Other-service Unmarried
## 5874 Married-civ-spouse Machine-op-inspct Husband
## 5875 Divorced Craft-repair Not-in-family
## 5876 Married-civ-spouse Machine-op-inspct Husband
## 5877 Never-married Sales Not-in-family
## 5878 Married-civ-spouse Farming-fishing Husband
## 5879 Never-married Other-service Not-in-family
## 5880 Never-married Craft-repair Own-child
## 5881 Married-civ-spouse Transport-moving Husband
## 5882 Married-civ-spouse Machine-op-inspct Husband
## 5883 Married-civ-spouse Sales Husband
## 5884 Never-married Craft-repair Not-in-family
## 5885 Divorced Sales Unmarried
## 5886 Married-civ-spouse Craft-repair Husband
## 5887 Married-civ-spouse Craft-repair Husband
## 5888 Never-married Exec-managerial Not-in-family
## 5889 Never-married Adm-clerical Other-relative
## 5890 Married-civ-spouse Prof-specialty Wife
## 5891 Never-married Sales Own-child
## 5892 Never-married Other-service Not-in-family
## 5893 Never-married Adm-clerical Own-child
## 5894 Never-married Adm-clerical Not-in-family
## 5895 Married-civ-spouse Protective-serv Husband
## 5896 Never-married Adm-clerical Other-relative
## 5897 Never-married Adm-clerical Own-child
## 5898 Never-married Transport-moving Own-child
## 5899 Married-civ-spouse Adm-clerical Husband
## 5900 Married-civ-spouse Craft-repair Other-relative
## 5901 Married-civ-spouse Craft-repair Husband
## 5902 Married-civ-spouse Exec-managerial Wife
## 5903 Married-civ-spouse Sales Husband
## 5904 Married-civ-spouse Transport-moving Wife
## 5905 Married-civ-spouse Handlers-cleaners Husband
## 5906 Married-civ-spouse Craft-repair Husband
## 5907 Married-civ-spouse Other-service Husband
## 5908 Never-married Exec-managerial Not-in-family
## 5909 Married-civ-spouse Adm-clerical Wife
## 5910 Divorced Craft-repair Unmarried
## 5911 Never-married Machine-op-inspct Own-child
## 5912 Never-married Adm-clerical Own-child
## 5913 Never-married Exec-managerial Not-in-family
## 5914 Never-married Other-service Own-child
## 5915 Married-civ-spouse Adm-clerical Wife
## 5916 Never-married Exec-managerial Not-in-family
## 5917 Married-civ-spouse ? Husband
## 5918 Married-civ-spouse Craft-repair Husband
## 5919 Married-civ-spouse Craft-repair Husband
## 5920 Divorced Machine-op-inspct Not-in-family
## 5921 Separated Other-service Unmarried
## 5922 Separated ? Unmarried
## 5923 Married-civ-spouse Transport-moving Husband
## 5924 Married-civ-spouse Craft-repair Husband
## 5925 Separated Prof-specialty Unmarried
## 5926 Married-civ-spouse Tech-support Husband
## 5927 Divorced Prof-specialty Not-in-family
## 5928 Never-married Transport-moving Own-child
## 5929 Never-married ? Own-child
## 5930 Married-civ-spouse Sales Husband
## 5931 Married-civ-spouse Sales Husband
## 5932 Married-civ-spouse Prof-specialty Wife
## 5933 Never-married Handlers-cleaners Own-child
## 5934 Married-civ-spouse Craft-repair Husband
## 5935 Divorced Adm-clerical Unmarried
## 5936 Never-married Adm-clerical Not-in-family
## 5937 Married-spouse-absent Adm-clerical Not-in-family
## 5938 Widowed Sales Not-in-family
## 5939 Never-married Craft-repair Own-child
## 5940 Married-civ-spouse Sales Husband
## 5941 Divorced Craft-repair Not-in-family
## 5942 Married-civ-spouse Other-service Wife
## 5943 Never-married Handlers-cleaners Not-in-family
## 5944 Married-civ-spouse Prof-specialty Husband
## 5945 Never-married Prof-specialty Not-in-family
## 5946 Never-married Sales Own-child
## 5947 Married-civ-spouse Craft-repair Husband
## 5948 Married-civ-spouse Exec-managerial Husband
## 5949 Married-civ-spouse Prof-specialty Wife
## 5950 Never-married Tech-support Own-child
## 5951 Divorced Sales Unmarried
## 5952 Never-married Prof-specialty Not-in-family
## 5953 Never-married Prof-specialty Not-in-family
## 5954 Never-married Adm-clerical Unmarried
## 5955 Never-married Adm-clerical Not-in-family
## 5956 Married-civ-spouse Machine-op-inspct Wife
## 5957 Married-civ-spouse Craft-repair Husband
## 5958 Married-civ-spouse Craft-repair Husband
## 5959 Married-civ-spouse Machine-op-inspct Husband
## 5960 Married-civ-spouse Other-service Wife
## 5961 Never-married Adm-clerical Own-child
## 5962 Married-civ-spouse Craft-repair Wife
## 5963 Separated Exec-managerial Own-child
## 5964 Never-married Priv-house-serv Unmarried
## 5965 Separated Exec-managerial Not-in-family
## 5966 Married-civ-spouse Exec-managerial Husband
## 5967 Divorced Craft-repair Other-relative
## 5968 Never-married Transport-moving Own-child
## 5969 Married-civ-spouse Prof-specialty Husband
## 5970 Never-married Machine-op-inspct Other-relative
## 5971 Never-married Prof-specialty Not-in-family
## 5972 Married-civ-spouse Machine-op-inspct Husband
## 5973 Married-civ-spouse Craft-repair Husband
## 5974 Divorced Other-service Not-in-family
## 5975 Married-civ-spouse Machine-op-inspct Husband
## 5976 Married-civ-spouse Farming-fishing Husband
## 5977 Married-civ-spouse Exec-managerial Husband
## 5978 Married-civ-spouse ? Husband
## 5979 Never-married Farming-fishing Not-in-family
## 5980 Never-married Transport-moving Not-in-family
## 5981 Married-civ-spouse Exec-managerial Husband
## 5982 Divorced Transport-moving Own-child
## 5983 Never-married Transport-moving Own-child
## 5984 Never-married ? Own-child
## 5985 Never-married Prof-specialty Not-in-family
## 5986 Married-civ-spouse Exec-managerial Husband
## 5987 Widowed Other-service Not-in-family
## 5988 Divorced Craft-repair Unmarried
## 5989 Divorced Machine-op-inspct Not-in-family
## 5990 Married-civ-spouse Craft-repair Husband
## 5991 Never-married Sales Own-child
## 5992 Divorced Other-service Own-child
## 5993 Never-married Handlers-cleaners Unmarried
## 5994 Divorced Other-service Not-in-family
## 5995 Married-civ-spouse Machine-op-inspct Husband
## 5996 Never-married Tech-support Own-child
## 5997 Never-married Transport-moving Own-child
## 5998 Married-civ-spouse Craft-repair Husband
## 5999 Divorced Craft-repair Not-in-family
## 6000 Never-married Craft-repair Other-relative
## 6001 Married-civ-spouse Exec-managerial Husband
## 6002 Divorced Adm-clerical Unmarried
## 6003 Married-civ-spouse Prof-specialty Wife
## 6004 Married-civ-spouse Machine-op-inspct Husband
## 6005 Married-civ-spouse Sales Husband
## 6006 Married-civ-spouse Exec-managerial Husband
## 6007 Married-civ-spouse Farming-fishing Husband
## 6008 Divorced Exec-managerial Unmarried
## 6009 Married-civ-spouse Tech-support Husband
## 6010 Never-married Machine-op-inspct Not-in-family
## 6011 Married-civ-spouse Sales Wife
## 6012 Married-civ-spouse Exec-managerial Husband
## 6013 Never-married Prof-specialty Not-in-family
## 6014 Divorced Sales Not-in-family
## 6015 Never-married Handlers-cleaners Own-child
## 6016 Widowed ? Not-in-family
## 6017 Married-civ-spouse Sales Husband
## 6018 Married-civ-spouse Sales Husband
## 6019 Never-married Tech-support Own-child
## 6020 Married-civ-spouse Sales Husband
## 6021 Divorced Exec-managerial Own-child
## 6022 Widowed Other-service Not-in-family
## 6023 Married-civ-spouse Transport-moving Husband
## 6024 Married-civ-spouse Handlers-cleaners Husband
## 6025 Married-civ-spouse Exec-managerial Husband
## 6026 Married-civ-spouse Prof-specialty Husband
## 6027 Married-civ-spouse Craft-repair Husband
## 6028 Married-civ-spouse Farming-fishing Husband
## 6029 Never-married Other-service Not-in-family
## 6030 Married-spouse-absent Prof-specialty Not-in-family
## 6031 Married-civ-spouse Sales Husband
## 6032 Separated Sales Not-in-family
## 6033 Never-married Adm-clerical Not-in-family
## 6034 Married-civ-spouse Adm-clerical Husband
## 6035 Never-married Adm-clerical Own-child
## 6036 Never-married Exec-managerial Not-in-family
## 6037 Married-civ-spouse Transport-moving Husband
## 6038 Married-civ-spouse Transport-moving Husband
## 6039 Married-civ-spouse Other-service Husband
## 6040 Widowed ? Not-in-family
## 6041 Married-civ-spouse Tech-support Husband
## 6042 Never-married Other-service Not-in-family
## 6043 Married-civ-spouse Sales Husband
## 6044 Never-married Other-service Own-child
## 6045 Never-married Adm-clerical Other-relative
## 6046 Married-civ-spouse Craft-repair Husband
## 6047 Married-civ-spouse Adm-clerical Wife
## 6048 Never-married Sales Unmarried
## 6049 Never-married Adm-clerical Not-in-family
## 6050 Married-civ-spouse Transport-moving Husband
## 6051 Married-civ-spouse Craft-repair Husband
## 6052 Divorced ? Not-in-family
## 6053 Divorced Other-service Unmarried
## 6054 Never-married Adm-clerical Not-in-family
## 6055 Divorced Machine-op-inspct Not-in-family
## 6056 Divorced Exec-managerial Not-in-family
## 6057 Divorced Sales Not-in-family
## 6058 Married-spouse-absent Farming-fishing Unmarried
## 6059 Divorced Craft-repair Not-in-family
## 6060 Divorced ? Unmarried
## 6061 Divorced Adm-clerical Unmarried
## 6062 Married-civ-spouse Sales Husband
## 6063 Married-civ-spouse Prof-specialty Husband
## 6064 Never-married Prof-specialty Own-child
## 6065 Married-civ-spouse Exec-managerial Husband
## 6066 Married-civ-spouse Exec-managerial Husband
## 6067 Married-civ-spouse Prof-specialty Husband
## 6068 Married-civ-spouse Machine-op-inspct Husband
## 6069 Never-married Exec-managerial Own-child
## 6070 Never-married Transport-moving Not-in-family
## 6071 Married-civ-spouse Farming-fishing Husband
## 6072 Married-civ-spouse Craft-repair Husband
## 6073 Divorced Adm-clerical Not-in-family
## 6074 Married-civ-spouse Transport-moving Husband
## 6075 Widowed Adm-clerical Unmarried
## 6076 Married-civ-spouse Craft-repair Husband
## 6077 Never-married Prof-specialty Not-in-family
## 6078 Never-married Prof-specialty Not-in-family
## 6079 Divorced Craft-repair Not-in-family
## 6080 Never-married Other-service Not-in-family
## 6081 Married-civ-spouse Exec-managerial Husband
## 6082 Never-married Machine-op-inspct Not-in-family
## 6083 Married-civ-spouse Prof-specialty Wife
## 6084 Never-married Adm-clerical Own-child
## 6085 Married-civ-spouse Transport-moving Husband
## 6086 Married-civ-spouse Transport-moving Husband
## 6087 Married-civ-spouse Transport-moving Husband
## 6088 Never-married Adm-clerical Own-child
## 6089 Never-married Sales Own-child
## 6090 Married-civ-spouse Adm-clerical Husband
## 6091 Divorced Other-service Unmarried
## 6092 Married-civ-spouse Craft-repair Husband
## 6093 Married-civ-spouse Tech-support Wife
## 6094 Never-married Sales Not-in-family
## 6095 Divorced Other-service Unmarried
## 6096 Divorced Other-service Unmarried
## 6097 Separated Machine-op-inspct Not-in-family
## 6098 Married-civ-spouse Craft-repair Husband
## 6099 Divorced Adm-clerical Unmarried
## 6100 Married-civ-spouse Handlers-cleaners Husband
## 6101 Married-civ-spouse Craft-repair Husband
## 6102 Married-civ-spouse Prof-specialty Husband
## 6103 Never-married Farming-fishing Own-child
## 6104 Married-civ-spouse Craft-repair Husband
## 6105 Widowed Other-service Unmarried
## 6106 Married-civ-spouse Exec-managerial Husband
## 6107 Never-married Other-service Unmarried
## 6108 Never-married Other-service Own-child
## 6109 Never-married Craft-repair Not-in-family
## 6110 Married-civ-spouse Machine-op-inspct Husband
## 6111 Never-married Adm-clerical Not-in-family
## 6112 Married-civ-spouse Exec-managerial Husband
## 6113 Married-civ-spouse Craft-repair Husband
## 6114 Never-married Prof-specialty Not-in-family
## 6115 Married-civ-spouse Adm-clerical Husband
## 6116 Married-civ-spouse Protective-serv Husband
## 6117 Married-civ-spouse Exec-managerial Husband
## 6118 Married-civ-spouse Machine-op-inspct Husband
## 6119 Never-married Transport-moving Not-in-family
## 6120 Married-civ-spouse Transport-moving Other-relative
## 6121 Married-spouse-absent Exec-managerial Not-in-family
## 6122 Married-civ-spouse Prof-specialty Husband
## 6123 Never-married Sales Own-child
## 6124 Widowed Other-service Unmarried
## 6125 Married-civ-spouse Craft-repair Husband
## 6126 Never-married Tech-support Not-in-family
## 6127 Divorced Other-service Not-in-family
## 6128 Divorced Adm-clerical Unmarried
## 6129 Never-married Exec-managerial Own-child
## 6130 Married-civ-spouse Prof-specialty Husband
## 6131 Divorced Prof-specialty Not-in-family
## 6132 Married-civ-spouse ? Wife
## 6133 Married-civ-spouse Farming-fishing Husband
## 6134 Divorced Tech-support Not-in-family
## 6135 Married-civ-spouse Adm-clerical Husband
## 6136 Married-civ-spouse Exec-managerial Husband
## 6137 Married-civ-spouse Adm-clerical Husband
## 6138 Married-civ-spouse Sales Husband
## 6139 Never-married Other-service Not-in-family
## 6140 Married-civ-spouse Craft-repair Husband
## 6141 Never-married Other-service Not-in-family
## 6142 Married-civ-spouse Transport-moving Husband
## 6143 Married-civ-spouse Other-service Husband
## 6144 Married-civ-spouse Prof-specialty Wife
## 6145 Divorced Adm-clerical Unmarried
## 6146 Married-civ-spouse Craft-repair Husband
## 6147 Divorced Exec-managerial Not-in-family
## 6148 Married-civ-spouse Exec-managerial Husband
## 6149 Never-married Other-service Own-child
## 6150 Married-civ-spouse Exec-managerial Husband
## 6151 Married-civ-spouse Exec-managerial Wife
## 6152 Married-civ-spouse Craft-repair Husband
## 6153 Separated Other-service Unmarried
## 6154 Never-married Exec-managerial Not-in-family
## 6155 Never-married Sales Not-in-family
## 6156 Never-married Transport-moving Not-in-family
## 6157 Never-married Other-service Own-child
## 6158 Divorced Craft-repair Not-in-family
## 6159 Never-married Sales Not-in-family
## 6160 Never-married Other-service Not-in-family
## 6161 Never-married Prof-specialty Not-in-family
## 6162 Married-spouse-absent Sales Not-in-family
## 6163 Separated Exec-managerial Unmarried
## 6164 Never-married Sales Own-child
## 6165 Married-civ-spouse Craft-repair Husband
## 6166 Married-civ-spouse Transport-moving Husband
## 6167 Never-married Adm-clerical Own-child
## 6168 Divorced Protective-serv Not-in-family
## 6169 Never-married Adm-clerical Own-child
## 6170 Widowed Exec-managerial Not-in-family
## 6171 Married-civ-spouse Exec-managerial Husband
## 6172 Married-civ-spouse Handlers-cleaners Husband
## 6173 Divorced Other-service Not-in-family
## 6174 Married-civ-spouse Prof-specialty Husband
## 6175 Never-married Adm-clerical Own-child
## 6176 Never-married Craft-repair Unmarried
## 6177 Married-civ-spouse Sales Husband
## 6178 Married-civ-spouse Sales Husband
## 6179 Married-civ-spouse ? Husband
## 6180 Married-civ-spouse Craft-repair Husband
## 6181 Married-civ-spouse Farming-fishing Husband
## 6182 Married-civ-spouse Prof-specialty Husband
## 6183 Never-married Exec-managerial Not-in-family
## 6184 Separated Other-service Unmarried
## 6185 Married-civ-spouse Protective-serv Husband
## 6186 Never-married Adm-clerical Not-in-family
## 6187 Married-civ-spouse Other-service Wife
## 6188 Never-married Handlers-cleaners Unmarried
## 6189 Married-civ-spouse Adm-clerical Wife
## 6190 Married-civ-spouse Protective-serv Husband
## 6191 Never-married Other-service Own-child
## 6192 Married-civ-spouse Prof-specialty Wife
## 6193 Married-civ-spouse Adm-clerical Wife
## 6194 Separated Exec-managerial Unmarried
## 6195 Never-married Sales Own-child
## 6196 Married-civ-spouse Adm-clerical Husband
## 6197 Married-civ-spouse Tech-support Husband
## 6198 Married-civ-spouse Exec-managerial Wife
## 6199 Divorced Other-service Not-in-family
## 6200 Divorced Exec-managerial Not-in-family
## 6201 Married-civ-spouse Prof-specialty Husband
## 6202 Never-married Sales Not-in-family
## 6203 Married-civ-spouse Craft-repair Husband
## 6204 Separated Prof-specialty Unmarried
## 6205 Married-civ-spouse Craft-repair Husband
## 6206 Married-civ-spouse Exec-managerial Husband
## 6207 Married-civ-spouse Other-service Husband
## 6208 Married-civ-spouse Craft-repair Husband
## 6209 Never-married Exec-managerial Not-in-family
## 6210 Divorced Protective-serv Unmarried
## 6211 Never-married Craft-repair Not-in-family
## 6212 Never-married Adm-clerical Not-in-family
## 6213 Married-civ-spouse Tech-support Husband
## 6214 Married-civ-spouse Farming-fishing Husband
## 6215 Married-civ-spouse Farming-fishing Husband
## 6216 Never-married Sales Own-child
## 6217 Married-civ-spouse Prof-specialty Husband
## 6218 Married-civ-spouse Exec-managerial Husband
## 6219 Never-married Craft-repair Not-in-family
## 6220 Married-civ-spouse Adm-clerical Husband
## 6221 Married-civ-spouse Exec-managerial Husband
## 6222 Never-married Adm-clerical Not-in-family
## 6223 Divorced Sales Not-in-family
## 6224 Never-married Adm-clerical Own-child
## 6225 Married-civ-spouse Prof-specialty Wife
## 6226 Married-civ-spouse Adm-clerical Wife
## 6227 Never-married Prof-specialty Not-in-family
## 6228 Married-civ-spouse Exec-managerial Husband
## 6229 Divorced Exec-managerial Not-in-family
## 6230 Never-married Adm-clerical Not-in-family
## 6231 Married-civ-spouse Handlers-cleaners Husband
## 6232 Married-civ-spouse ? Husband
## 6233 Married-civ-spouse Prof-specialty Husband
## 6234 Married-civ-spouse Prof-specialty Husband
## 6235 Married-civ-spouse Craft-repair Husband
## 6236 Divorced Adm-clerical Not-in-family
## 6237 Married-civ-spouse Exec-managerial Husband
## 6238 Married-civ-spouse Farming-fishing Husband
## 6239 Never-married Handlers-cleaners Not-in-family
## 6240 Separated Adm-clerical Other-relative
## 6241 Married-civ-spouse Sales Husband
## 6242 Married-spouse-absent Prof-specialty Own-child
## 6243 Never-married Prof-specialty Not-in-family
## 6244 Never-married Prof-specialty Not-in-family
## 6245 Never-married Other-service Own-child
## 6246 Married-civ-spouse Craft-repair Husband
## 6247 Never-married Craft-repair Own-child
## 6248 Never-married Sales Own-child
## 6249 Married-civ-spouse Exec-managerial Husband
## 6250 Never-married Adm-clerical Own-child
## 6251 Married-civ-spouse Craft-repair Husband
## 6252 Never-married Sales Own-child
## 6253 Never-married Sales Own-child
## 6254 Widowed Sales Unmarried
## 6255 Never-married Adm-clerical Not-in-family
## 6256 Married-civ-spouse Craft-repair Husband
## 6257 Married-civ-spouse Exec-managerial Husband
## 6258 Never-married Other-service Own-child
## 6259 Never-married Prof-specialty Not-in-family
## 6260 Separated Protective-serv Not-in-family
## 6261 Divorced Craft-repair Other-relative
## 6262 Separated Adm-clerical Unmarried
## 6263 Never-married Handlers-cleaners Own-child
## 6264 Married-civ-spouse Prof-specialty Husband
## 6265 Married-civ-spouse Craft-repair Husband
## 6266 Married-civ-spouse Farming-fishing Husband
## 6267 Married-civ-spouse Craft-repair Husband
## 6268 Never-married Other-service Own-child
## 6269 Never-married Other-service Own-child
## 6270 Married-civ-spouse Prof-specialty Other-relative
## 6271 Married-civ-spouse Craft-repair Husband
## 6272 Never-married Adm-clerical Unmarried
## 6273 Divorced Sales Own-child
## 6274 Never-married Adm-clerical Own-child
## 6275 Married-civ-spouse Craft-repair Husband
## 6276 Married-civ-spouse Sales Husband
## 6277 Never-married Protective-serv Own-child
## 6278 Divorced Protective-serv Not-in-family
## 6279 Married-civ-spouse Craft-repair Husband
## 6280 Married-civ-spouse Prof-specialty Husband
## 6281 Divorced Craft-repair Not-in-family
## 6282 Married-civ-spouse Exec-managerial Husband
## 6283 Married-civ-spouse Transport-moving Husband
## 6284 Never-married Sales Own-child
## 6285 Never-married ? Not-in-family
## 6286 Never-married ? Own-child
## 6287 Divorced Adm-clerical Not-in-family
## 6288 Never-married Adm-clerical Not-in-family
## 6289 Never-married Exec-managerial Not-in-family
## 6290 Married-civ-spouse Sales Husband
## 6291 Never-married Sales Not-in-family
## 6292 Married-civ-spouse Adm-clerical Husband
## 6293 Never-married Sales Own-child
## 6294 Never-married Adm-clerical Not-in-family
## 6295 Never-married Other-service Unmarried
## 6296 Married-civ-spouse Exec-managerial Wife
## 6297 Never-married Prof-specialty Not-in-family
## 6298 Separated Other-service Unmarried
## 6299 Divorced Other-service Not-in-family
## 6300 Married-civ-spouse Prof-specialty Husband
## 6301 Never-married Craft-repair Not-in-family
## 6302 Married-civ-spouse Craft-repair Husband
## 6303 Married-civ-spouse Machine-op-inspct Husband
## 6304 Married-civ-spouse Sales Husband
## 6305 Married-civ-spouse Prof-specialty Husband
## 6306 Never-married Adm-clerical Own-child
## 6307 Married-civ-spouse Transport-moving Husband
## 6308 Married-civ-spouse Sales Husband
## 6309 Never-married Exec-managerial Not-in-family
## 6310 Divorced Sales Not-in-family
## 6311 Married-civ-spouse Tech-support Wife
## 6312 Married-civ-spouse Craft-repair Husband
## 6313 Never-married Sales Own-child
## 6314 Divorced Exec-managerial Not-in-family
## 6315 Married-civ-spouse ? Husband
## 6316 Separated Exec-managerial Unmarried
## 6317 Never-married Other-service Own-child
## 6318 Divorced Adm-clerical Unmarried
## 6319 Married-civ-spouse Prof-specialty Husband
## 6320 Married-spouse-absent Other-service Not-in-family
## 6321 Married-civ-spouse Exec-managerial Husband
## 6322 Never-married Sales Own-child
## 6323 Married-civ-spouse Farming-fishing Husband
## 6324 Never-married Tech-support Own-child
## 6325 Married-civ-spouse Sales Husband
## 6326 Married-civ-spouse Prof-specialty Husband
## 6327 Divorced Other-service Unmarried
## 6328 Never-married Adm-clerical Not-in-family
## 6329 Never-married Adm-clerical Own-child
## 6330 Never-married Adm-clerical Not-in-family
## 6331 Married-civ-spouse Craft-repair Husband
## 6332 Married-civ-spouse Exec-managerial Wife
## 6333 Married-civ-spouse Prof-specialty Husband
## 6334 Divorced Adm-clerical Unmarried
## 6335 Divorced Prof-specialty Not-in-family
## 6336 Married-civ-spouse Sales Wife
## 6337 Divorced Prof-specialty Unmarried
## 6338 Married-civ-spouse Transport-moving Husband
## 6339 Never-married Protective-serv Own-child
## 6340 Married-civ-spouse Tech-support Wife
## 6341 Divorced Exec-managerial Own-child
## 6342 Never-married Sales Not-in-family
## 6343 Married-civ-spouse ? Husband
## 6344 Married-civ-spouse Exec-managerial Husband
## 6345 Married-civ-spouse Adm-clerical Husband
## 6346 Divorced Craft-repair Unmarried
## 6347 Never-married Exec-managerial Own-child
## 6348 Never-married Sales Not-in-family
## 6349 Never-married Sales Other-relative
## 6350 Married-civ-spouse Prof-specialty Husband
## 6351 Divorced Adm-clerical Unmarried
## 6352 Never-married ? Unmarried
## 6353 Married-civ-spouse Craft-repair Husband
## 6354 Married-civ-spouse Transport-moving Husband
## 6355 Married-civ-spouse Adm-clerical Wife
## 6356 Married-civ-spouse Sales Husband
## 6357 Never-married Sales Not-in-family
## 6358 Divorced Craft-repair Not-in-family
## 6359 Never-married Adm-clerical Not-in-family
## 6360 Married-civ-spouse Prof-specialty Husband
## 6361 Never-married Priv-house-serv Not-in-family
## 6362 Divorced Exec-managerial Not-in-family
## 6363 Married-civ-spouse Machine-op-inspct Husband
## 6364 Married-civ-spouse Farming-fishing Husband
## 6365 Never-married Exec-managerial Unmarried
## 6366 Married-civ-spouse Adm-clerical Husband
## 6367 Never-married Machine-op-inspct Not-in-family
## 6368 Never-married Adm-clerical Unmarried
## 6369 Never-married Handlers-cleaners Own-child
## 6370 Married-civ-spouse Transport-moving Husband
## 6371 Never-married Adm-clerical Unmarried
## 6372 Married-civ-spouse Craft-repair Husband
## 6373 Never-married Exec-managerial Not-in-family
## 6374 Married-civ-spouse Farming-fishing Husband
## 6375 Never-married Exec-managerial Not-in-family
## 6376 Married-civ-spouse Farming-fishing Husband
## 6377 Never-married Prof-specialty Own-child
## 6378 Married-civ-spouse Transport-moving Husband
## 6379 Married-civ-spouse Prof-specialty Husband
## 6380 Divorced Adm-clerical Not-in-family
## 6381 Never-married Transport-moving Not-in-family
## 6382 Married-civ-spouse Other-service Husband
## 6383 Married-civ-spouse Exec-managerial Husband
## 6384 Married-civ-spouse Exec-managerial Husband
## 6385 Married-civ-spouse Prof-specialty Husband
## 6386 Divorced Exec-managerial Unmarried
## 6387 Never-married Other-service Own-child
## 6388 Married-civ-spouse Prof-specialty Husband
## 6389 Never-married Craft-repair Not-in-family
## 6390 Never-married Handlers-cleaners Own-child
## 6391 Married-civ-spouse Farming-fishing Husband
## 6392 Married-civ-spouse Prof-specialty Wife
## 6393 Separated Prof-specialty Not-in-family
## 6394 Separated Priv-house-serv Unmarried
## 6395 Never-married Adm-clerical Own-child
## 6396 Never-married Tech-support Own-child
## 6397 Never-married Handlers-cleaners Own-child
## 6398 Married-civ-spouse Exec-managerial Husband
## 6399 Never-married Other-service Not-in-family
## 6400 Never-married Transport-moving Own-child
## 6401 Never-married Tech-support Own-child
## 6402 Divorced Adm-clerical Unmarried
## 6403 Never-married Prof-specialty Not-in-family
## 6404 Never-married Prof-specialty Not-in-family
## 6405 Never-married Farming-fishing Own-child
## 6406 Divorced Other-service Not-in-family
## 6407 Married-civ-spouse Sales Husband
## 6408 Divorced ? Own-child
## 6409 Never-married Sales Not-in-family
## 6410 Never-married Exec-managerial Own-child
## 6411 Never-married Other-service Own-child
## 6412 Married-civ-spouse Craft-repair Husband
## 6413 Never-married Handlers-cleaners Own-child
## 6414 Married-civ-spouse Craft-repair Husband
## 6415 Married-civ-spouse Prof-specialty Husband
## 6416 Married-civ-spouse Exec-managerial Husband
## 6417 Widowed Adm-clerical Not-in-family
## 6418 Divorced Farming-fishing Not-in-family
## 6419 Married-civ-spouse Handlers-cleaners Husband
## 6420 Married-civ-spouse Exec-managerial Wife
## 6421 Never-married Sales Not-in-family
## 6422 Married-civ-spouse Prof-specialty Husband
## 6423 Separated Adm-clerical Not-in-family
## 6424 Married-civ-spouse Other-service Wife
## 6425 Married-civ-spouse Sales Wife
## 6426 Married-civ-spouse Craft-repair Husband
## 6427 Married-civ-spouse Sales Husband
## 6428 Never-married Other-service Not-in-family
## 6429 Never-married Other-service Own-child
## 6430 Married-civ-spouse Exec-managerial Husband
## 6431 Never-married Prof-specialty Not-in-family
## 6432 Never-married Craft-repair Other-relative
## 6433 Married-civ-spouse ? Husband
## 6434 Married-civ-spouse Farming-fishing Other-relative
## 6435 Divorced Craft-repair Unmarried
## 6436 Widowed Machine-op-inspct Unmarried
## 6437 Never-married Sales Other-relative
## 6438 Never-married Other-service Own-child
## 6439 Married-civ-spouse Sales Husband
## 6440 Widowed Other-service Not-in-family
## 6441 Never-married Craft-repair Own-child
## 6442 Never-married Prof-specialty Own-child
## 6443 Married-civ-spouse Exec-managerial Wife
## 6444 Never-married Exec-managerial Own-child
## 6445 Divorced Adm-clerical Not-in-family
## 6446 Never-married Machine-op-inspct Own-child
## 6447 Never-married Other-service Not-in-family
## 6448 Married-civ-spouse Exec-managerial Husband
## 6449 Never-married ? Own-child
## 6450 Divorced Adm-clerical Not-in-family
## 6451 Never-married Other-service Own-child
## 6452 Married-civ-spouse Craft-repair Husband
## 6453 Never-married Machine-op-inspct Not-in-family
## 6454 Never-married Exec-managerial Not-in-family
## 6455 Married-civ-spouse Handlers-cleaners Husband
## 6456 Married-civ-spouse Prof-specialty Husband
## 6457 Never-married Other-service Not-in-family
## 6458 Married-civ-spouse Transport-moving Husband
## 6459 Divorced Sales Unmarried
## 6460 Never-married Sales Other-relative
## 6461 Married-spouse-absent Adm-clerical Unmarried
## 6462 Widowed Adm-clerical Not-in-family
## 6463 Never-married Adm-clerical Not-in-family
## 6464 Married-civ-spouse Sales Husband
## 6465 Never-married Exec-managerial Not-in-family
## 6466 Married-civ-spouse Prof-specialty Husband
## 6467 Married-civ-spouse Craft-repair Husband
## 6468 Married-civ-spouse Transport-moving Husband
## 6469 Married-civ-spouse Exec-managerial Husband
## 6470 Divorced Adm-clerical Other-relative
## 6471 Never-married Adm-clerical Not-in-family
## 6472 Divorced Adm-clerical Own-child
## 6473 Married-civ-spouse Craft-repair Husband
## 6474 Married-civ-spouse Sales Husband
## 6475 Separated Prof-specialty Unmarried
## 6476 Never-married Prof-specialty Not-in-family
## 6477 Never-married Prof-specialty Not-in-family
## 6478 Married-civ-spouse Transport-moving Husband
## 6479 Married-civ-spouse Exec-managerial Husband
## 6480 Divorced Prof-specialty Not-in-family
## 6481 Never-married Sales Own-child
## 6482 Never-married Prof-specialty Not-in-family
## 6483 Married-civ-spouse Prof-specialty Husband
## 6484 Married-civ-spouse Exec-managerial Husband
## 6485 Married-civ-spouse Prof-specialty Husband
## 6486 Married-civ-spouse Sales Husband
## 6487 Married-civ-spouse Prof-specialty Husband
## 6488 Married-civ-spouse Prof-specialty Husband
## 6489 Married-civ-spouse Other-service Husband
## 6490 Never-married Sales Own-child
## 6491 Married-civ-spouse Craft-repair Husband
## 6492 Married-civ-spouse Transport-moving Husband
## 6493 Married-civ-spouse Prof-specialty Husband
## 6494 Widowed Exec-managerial Unmarried
## 6495 Divorced Exec-managerial Unmarried
## 6496 Married-civ-spouse Prof-specialty Husband
## 6497 Never-married Adm-clerical Own-child
## 6498 Married-civ-spouse Handlers-cleaners Husband
## 6499 Divorced Craft-repair Own-child
## 6500 Married-civ-spouse Craft-repair Husband
## 6501 Married-civ-spouse Prof-specialty Husband
## 6502 Never-married Farming-fishing Own-child
## 6503 Divorced Sales Not-in-family
## 6504 Never-married Tech-support Not-in-family
## 6505 Never-married Handlers-cleaners Own-child
## 6506 Separated Other-service Unmarried
## 6507 Married-civ-spouse Other-service Husband
## 6508 Married-civ-spouse Tech-support Husband
## 6509 Married-civ-spouse Craft-repair Husband
## 6510 Married-civ-spouse Machine-op-inspct Husband
## 6511 Married-civ-spouse ? Husband
## 6512 Never-married Other-service Unmarried
## 6513 Never-married Protective-serv Not-in-family
## 6514 Married-civ-spouse Sales Husband
## 6515 Married-civ-spouse Handlers-cleaners Husband
## 6516 Married-civ-spouse Sales Husband
## 6517 Divorced Adm-clerical Unmarried
## 6518 Never-married Transport-moving Not-in-family
## 6519 Married-civ-spouse Machine-op-inspct Husband
## 6520 Divorced Exec-managerial Unmarried
## 6521 Married-civ-spouse Transport-moving Husband
## 6522 Separated ? Unmarried
## 6523 Never-married Sales Own-child
## 6524 Divorced Adm-clerical Unmarried
## 6525 Divorced Prof-specialty Unmarried
## 6526 Never-married Sales Not-in-family
## 6527 Married-civ-spouse Adm-clerical Husband
## 6528 Never-married Transport-moving Not-in-family
## 6529 Married-civ-spouse Prof-specialty Husband
## 6530 Never-married Adm-clerical Not-in-family
## 6531 Married-civ-spouse Sales Husband
## 6532 Widowed Adm-clerical Not-in-family
## 6533 Married-civ-spouse Craft-repair Husband
## 6534 Never-married Prof-specialty Not-in-family
## 6535 Never-married Prof-specialty Own-child
## 6536 Never-married Sales Not-in-family
## 6537 Married-civ-spouse ? Husband
## 6538 Married-civ-spouse Craft-repair Husband
## 6539 Divorced Machine-op-inspct Not-in-family
## 6540 Separated Transport-moving Not-in-family
## 6541 Never-married Adm-clerical Not-in-family
## 6542 Married-civ-spouse Other-service Husband
## 6543 Never-married ? Own-child
## 6544 Divorced Sales Not-in-family
## 6545 Never-married Machine-op-inspct Unmarried
## 6546 Divorced Adm-clerical Not-in-family
## 6547 Married-civ-spouse Protective-serv Husband
## 6548 Widowed Sales Unmarried
## 6549 Married-civ-spouse Exec-managerial Husband
## 6550 Never-married ? Not-in-family
## 6551 Married-civ-spouse Handlers-cleaners Husband
## 6552 Divorced Tech-support Own-child
## 6553 Married-civ-spouse Transport-moving Husband
## 6554 Divorced Prof-specialty Unmarried
## 6555 Married-civ-spouse Transport-moving Husband
## 6556 Married-civ-spouse Craft-repair Husband
## 6557 Married-civ-spouse Prof-specialty Husband
## 6558 Never-married Tech-support Not-in-family
## 6559 Never-married ? Own-child
## 6560 Married-civ-spouse Farming-fishing Husband
## 6561 Never-married Prof-specialty Not-in-family
## 6562 Married-civ-spouse Prof-specialty Husband
## 6563 Married-civ-spouse Transport-moving Husband
## 6564 Married-civ-spouse Transport-moving Husband
## 6565 Never-married ? Own-child
## 6566 Never-married Adm-clerical Unmarried
## 6567 Married-civ-spouse Farming-fishing Husband
## 6568 Never-married Adm-clerical Not-in-family
## 6569 Married-civ-spouse Farming-fishing Husband
## 6570 Married-civ-spouse Tech-support Husband
## 6571 Married-civ-spouse Sales Own-child
## 6572 Never-married Farming-fishing Own-child
## 6573 Never-married Adm-clerical Not-in-family
## 6574 Married-civ-spouse Exec-managerial Husband
## 6575 Married-civ-spouse Tech-support Husband
## 6576 Never-married Handlers-cleaners Not-in-family
## 6577 Married-civ-spouse Sales Husband
## 6578 Married-civ-spouse Other-service Husband
## 6579 Separated Other-service Not-in-family
## 6580 Divorced Adm-clerical Unmarried
## 6581 Married-civ-spouse Exec-managerial Husband
## 6582 Separated Other-service Not-in-family
## 6583 Married-civ-spouse Craft-repair Husband
## 6584 Divorced Priv-house-serv Unmarried
## 6585 Never-married Craft-repair Not-in-family
## 6586 Married-civ-spouse Sales Husband
## 6587 Married-civ-spouse Exec-managerial Husband
## 6588 Never-married Other-service Other-relative
## 6589 Never-married Exec-managerial Other-relative
## 6590 Divorced Machine-op-inspct Own-child
## 6591 Married-spouse-absent Adm-clerical Not-in-family
## 6592 Never-married ? Own-child
## 6593 Widowed Sales Not-in-family
## 6594 Never-married Sales Own-child
## 6595 Never-married Other-service Unmarried
## 6596 Divorced Sales Unmarried
## 6597 Never-married Sales Not-in-family
## 6598 Never-married Handlers-cleaners Other-relative
## 6599 Married-civ-spouse Machine-op-inspct Husband
## 6600 Married-civ-spouse Tech-support Husband
## 6601 Married-civ-spouse Sales Husband
## 6602 Married-spouse-absent Other-service Not-in-family
## 6603 Widowed Machine-op-inspct Not-in-family
## 6604 Married-civ-spouse Sales Husband
## 6605 Never-married Other-service Not-in-family
## 6606 Never-married Other-service Not-in-family
## 6607 Married-civ-spouse Craft-repair Husband
## 6608 Married-civ-spouse Prof-specialty Husband
## 6609 Married-civ-spouse Exec-managerial Husband
## 6610 Married-civ-spouse Prof-specialty Husband
## 6611 Married-civ-spouse Protective-serv Husband
## 6612 Widowed Adm-clerical Unmarried
## 6613 Never-married Other-service Not-in-family
## 6614 Never-married Handlers-cleaners Other-relative
## 6615 Married-civ-spouse Tech-support Husband
## 6616 Never-married Exec-managerial Not-in-family
## 6617 Never-married Transport-moving Not-in-family
## 6618 Separated Sales Unmarried
## 6619 Married-civ-spouse Transport-moving Husband
## 6620 Separated Adm-clerical Own-child
## 6621 Married-civ-spouse Farming-fishing Husband
## 6622 Divorced Exec-managerial Not-in-family
## 6623 Married-civ-spouse Sales Husband
## 6624 Divorced Craft-repair Unmarried
## 6625 Married-civ-spouse Craft-repair Husband
## 6626 Married-civ-spouse Exec-managerial Husband
## 6627 Never-married Adm-clerical Unmarried
## 6628 Married-civ-spouse Craft-repair Husband
## 6629 Married-civ-spouse Transport-moving Husband
## 6630 Never-married Other-service Unmarried
## 6631 Married-spouse-absent Sales Own-child
## 6632 Married-civ-spouse Exec-managerial Husband
## 6633 Divorced Prof-specialty Unmarried
## 6634 Married-civ-spouse Prof-specialty Husband
## 6635 Never-married Sales Not-in-family
## 6636 Married-civ-spouse Craft-repair Husband
## 6637 Married-civ-spouse Transport-moving Husband
## 6638 Never-married Prof-specialty Not-in-family
## 6639 Married-civ-spouse Craft-repair Husband
## 6640 Married-civ-spouse Prof-specialty Husband
## 6641 Never-married ? Own-child
## 6642 Never-married Machine-op-inspct Own-child
## 6643 Never-married Adm-clerical Not-in-family
## 6644 Divorced Prof-specialty Not-in-family
## 6645 Widowed Craft-repair Unmarried
## 6646 Married-civ-spouse Sales Husband
## 6647 Separated ? Unmarried
## 6648 Never-married Adm-clerical Own-child
## 6649 Never-married Adm-clerical Not-in-family
## 6650 Married-civ-spouse Craft-repair Husband
## 6651 Never-married Handlers-cleaners Own-child
## 6652 Divorced Transport-moving Not-in-family
## 6653 Married-civ-spouse Machine-op-inspct Husband
## 6654 Married-civ-spouse Farming-fishing Husband
## 6655 Divorced Machine-op-inspct Unmarried
## 6656 Married-civ-spouse Craft-repair Husband
## 6657 Divorced Other-service Own-child
## 6658 Never-married Handlers-cleaners Own-child
## 6659 Married-civ-spouse Other-service Husband
## 6660 Married-civ-spouse Sales Husband
## 6661 Married-civ-spouse Sales Husband
## 6662 Married-civ-spouse Craft-repair Husband
## 6663 Never-married Adm-clerical Not-in-family
## 6664 Never-married ? Own-child
## 6665 Married-civ-spouse Prof-specialty Wife
## 6666 Married-civ-spouse Protective-serv Husband
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## native_region income
## 1 United-States <=50K
## 2 United-States <=50K
## 3 United-States <=50K
## 4 United-States <=50K
## 5 Cuba <=50K
## 6 United-States <=50K
## 7 Jamaica <=50K
## 8 United-States >50K
## 9 United-States >50K
## 10 United-States >50K
## 11 United-States >50K
## 12 India >50K
## 13 United-States <=50K
## 14 United-States <=50K
## 15 ? >50K
## 16 Mexico <=50K
## 17 United-States <=50K
## 18 United-States <=50K
## 19 United-States <=50K
## 20 United-States >50K
## 21 United-States >50K
## 22 United-States <=50K
## 23 United-States <=50K
## 24 United-States <=50K
## 25 United-States <=50K
## 26 United-States >50K
## 27 United-States <=50K
## 28 South >50K
## 29 United-States <=50K
## 30 United-States <=50K
## 31 United-States <=50K
## 32 United-States <=50K
## 33 United-States <=50K
## 34 United-States <=50K
## 35 United-States <=50K
## 36 Puerto-Rico <=50K
## 37 United-States <=50K
## 38 United-States <=50K
## 39 ? >50K
## 40 United-States <=50K
## 41 United-States <=50K
## 42 United-States <=50K
## 43 United-States <=50K
## 44 United-States <=50K
## 45 United-States <=50K
## 46 United-States >50K
## 47 United-States <=50K
## 48 United-States <=50K
## 49 United-States <=50K
## 50 United-States <=50K
## 51 United-States <=50K
## 52 ? <=50K
## 53 Honduras >50K
## 54 United-States >50K
## 55 United-States <=50K
## 56 United-States >50K
## 57 Mexico <=50K
## 58 Puerto-Rico <=50K
## 59 United-States <=50K
## 60 United-States <=50K
## 61 United-States <=50K
## 62 ? <=50K
## 63 United-States <=50K
## 64 United-States >50K
## 65 United-States <=50K
## 66 United-States <=50K
## 67 United-States <=50K
## 68 United-States >50K
## 69 United-States >50K
## 70 United-States <=50K
## 71 United-States <=50K
## 72 United-States <=50K
## 73 United-States >50K
## 74 United-States <=50K
## 75 United-States <=50K
## 76 Mexico <=50K
## 77 United-States <=50K
## 78 United-States <=50K
## 79 United-States <=50K
## 80 United-States <=50K
## 81 United-States <=50K
## 82 Cuba <=50K
## 83 United-States <=50K
## 84 United-States <=50K
## 85 United-States >50K
## 86 United-States <=50K
## 87 United-States >50K
## 88 United-States <=50K
## 89 United-States <=50K
## 90 United-States >50K
## 91 United-States <=50K
## 92 United-States <=50K
## 93 United-States <=50K
## 94 ? <=50K
## 95 United-States >50K
## 96 United-States <=50K
## 97 United-States >50K
## 98 United-States >50K
## 99 England <=50K
## 100 United-States <=50K
## 101 United-States >50K
## 102 United-States >50K
## 103 United-States <=50K
## 104 United-States <=50K
## 105 United-States <=50K
## 106 United-States >50K
## 107 United-States <=50K
## 108 United-States <=50K
## 109 United-States <=50K
## 110 United-States <=50K
## 111 United-States <=50K
## 112 United-States >50K
## 113 Canada >50K
## 114 United-States <=50K
## 115 United-States <=50K
## 116 United-States <=50K
## 117 United-States <=50K
## 118 United-States >50K
## 119 United-States <=50K
## 120 United-States <=50K
## 121 United-States <=50K
## 122 United-States <=50K
## 123 Germany <=50K
## 124 United-States >50K
## 125 United-States <=50K
## 126 United-States >50K
## 127 United-States <=50K
## 128 United-States >50K
## 129 United-States <=50K
## 130 United-States <=50K
## 131 United-States <=50K
## 132 United-States <=50K
## 133 United-States <=50K
## 134 United-States <=50K
## 135 United-States <=50K
## 136 Iran >50K
## 137 United-States <=50K
## 138 United-States <=50K
## 139 United-States <=50K
## 140 United-States >50K
## 141 United-States <=50K
## 142 United-States <=50K
## 143 Mexico <=50K
## 144 United-States >50K
## 145 United-States <=50K
## 146 United-States <=50K
## 147 United-States <=50K
## 148 United-States <=50K
## 149 United-States <=50K
## 150 United-States <=50K
## 151 Mexico <=50K
## 152 United-States <=50K
## 153 Philippines <=50K
## 154 United-States <=50K
## 155 United-States >50K
## 156 United-States <=50K
## 157 United-States <=50K
## 158 United-States <=50K
## 159 United-States <=50K
## 160 United-States <=50K
## 161 United-States <=50K
## 162 United-States <=50K
## 163 United-States <=50K
## 164 England <=50K
## 165 United-States >50K
## 166 Mexico <=50K
## 167 United-States <=50K
## 168 United-States <=50K
## 169 United-States <=50K
## 170 United-States <=50K
## 171 United-States <=50K
## 172 United-States <=50K
## 173 United-States >50K
## 174 United-States <=50K
## 175 United-States >50K
## 176 United-States <=50K
## 177 United-States <=50K
## 178 United-States <=50K
## 179 United-States <=50K
## 180 United-States <=50K
## 181 United-States >50K
## 182 United-States <=50K
## 183 United-States <=50K
## 184 Mexico >50K
## 185 United-States >50K
## 186 United-States <=50K
## 187 United-States <=50K
## 188 United-States <=50K
## 189 United-States <=50K
## 190 United-States >50K
## 191 United-States <=50K
## 192 United-States <=50K
## 193 United-States <=50K
## 194 United-States <=50K
## 195 United-States <=50K
## 196 Mexico <=50K
## 197 United-States <=50K
## 198 United-States >50K
## 199 United-States >50K
## 200 United-States <=50K
## 201 United-States <=50K
## 202 Italy <=50K
## 203 United-States >50K
## 204 United-States <=50K
## 205 United-States <=50K
## 206 United-States <=50K
## 207 United-States >50K
## 208 United-States <=50K
## 209 United-States >50K
## 210 United-States <=50K
## 211 United-States <=50K
## 212 United-States >50K
## 213 United-States <=50K
## 214 United-States <=50K
## 215 United-States >50K
## 216 United-States >50K
## 217 United-States <=50K
## 218 Poland <=50K
## 219 United-States <=50K
## 220 United-States <=50K
## 221 United-States <=50K
## 222 United-States <=50K
## 223 United-States <=50K
## 224 United-States <=50K
## 225 United-States <=50K
## 226 United-States <=50K
## 227 United-States <=50K
## 228 United-States <=50K
## 229 Columbia <=50K
## 230 United-States <=50K
## 231 United-States <=50K
## 232 United-States <=50K
## 233 United-States <=50K
## 234 United-States <=50K
## 235 United-States >50K
## 236 United-States <=50K
## 237 United-States <=50K
## 238 Iran >50K
## 239 United-States >50K
## 240 United-States <=50K
## 241 United-States >50K
## 242 United-States <=50K
## 243 United-States <=50K
## 244 United-States <=50K
## 245 United-States <=50K
## 246 ? <=50K
## 247 United-States >50K
## 248 United-States <=50K
## 249 United-States >50K
## 250 ? <=50K
## 251 United-States >50K
## 252 United-States <=50K
## 253 United-States <=50K
## 254 United-States <=50K
## 255 United-States <=50K
## 256 Cambodia >50K
## 257 United-States <=50K
## 258 United-States <=50K
## 259 United-States <=50K
## 260 United-States <=50K
## 261 United-States <=50K
## 262 United-States <=50K
## 263 United-States <=50K
## 264 United-States <=50K
## 265 United-States <=50K
## 266 Thailand >50K
## 267 Canada <=50K
## 268 United-States >50K
## 269 United-States <=50K
## 270 United-States >50K
## 271 United-States >50K
## 272 United-States <=50K
## 273 United-States <=50K
## 274 United-States <=50K
## 275 United-States <=50K
## 276 United-States <=50K
## 277 United-States >50K
## 278 United-States <=50K
## 279 United-States <=50K
## 280 United-States >50K
## 281 Germany <=50K
## 282 United-States >50K
## 283 United-States <=50K
## 284 United-States <=50K
## 285 United-States <=50K
## 286 United-States >50K
## 287 United-States >50K
## 288 United-States <=50K
## 289 United-States <=50K
## 290 Ecuador <=50K
## 291 United-States <=50K
## 292 United-States <=50K
## 293 United-States <=50K
## 294 United-States <=50K
## 295 United-States <=50K
## 296 United-States <=50K
## 297 United-States <=50K
## 298 ? <=50K
## 299 United-States <=50K
## 300 United-States <=50K
## 301 United-States >50K
## 302 United-States <=50K
## 303 United-States >50K
## 304 United-States >50K
## 305 Laos <=50K
## 306 United-States <=50K
## 307 United-States <=50K
## 308 United-States <=50K
## 309 United-States >50K
## 310 United-States >50K
## 311 United-States <=50K
## 312 Taiwan >50K
## 313 United-States <=50K
## 314 United-States >50K
## 315 United-States <=50K
## 316 United-States <=50K
## 317 United-States <=50K
## 318 Mexico <=50K
## 319 United-States <=50K
## 320 United-States <=50K
## 321 United-States <=50K
## 322 United-States <=50K
## 323 United-States <=50K
## 324 United-States <=50K
## 325 United-States <=50K
## 326 United-States <=50K
## 327 United-States <=50K
## 328 United-States >50K
## 329 United-States <=50K
## 330 United-States <=50K
## 331 United-States <=50K
## 332 United-States <=50K
## 333 United-States <=50K
## 334 United-States <=50K
## 335 United-States <=50K
## 336 Mexico <=50K
## 337 United-States <=50K
## 338 United-States <=50K
## 339 Haiti <=50K
## 340 United-States <=50K
## 341 United-States <=50K
## 342 United-States >50K
## 343 United-States >50K
## 344 United-States <=50K
## 345 United-States <=50K
## 346 United-States <=50K
## 347 United-States <=50K
## 348 United-States <=50K
## 349 Iran <=50K
## 350 United-States <=50K
## 351 United-States >50K
## 352 United-States <=50K
## 353 United-States >50K
## 354 United-States <=50K
## 355 United-States >50K
## 356 United-States >50K
## 357 United-States <=50K
## 358 United-States >50K
## 359 United-States <=50K
## 360 Portugal <=50K
## 361 United-States <=50K
## 362 United-States >50K
## 363 United-States <=50K
## 364 United-States >50K
## 365 United-States <=50K
## 366 United-States <=50K
## 367 United-States <=50K
## 368 United-States <=50K
## 369 United-States >50K
## 370 United-States <=50K
## 371 United-States <=50K
## 372 United-States <=50K
## 373 United-States >50K
## 374 United-States <=50K
## 375 United-States <=50K
## 376 United-States <=50K
## 377 United-States <=50K
## 378 United-States <=50K
## 379 United-States <=50K
## 380 United-States <=50K
## 381 United-States <=50K
## 382 United-States <=50K
## 383 United-States <=50K
## 384 United-States <=50K
## 385 United-States <=50K
## 386 United-States <=50K
## 387 United-States <=50K
## 388 United-States >50K
## 389 United-States <=50K
## 390 United-States <=50K
## 391 United-States >50K
## 392 United-States <=50K
## 393 United-States <=50K
## 394 ? >50K
## 395 United-States <=50K
## 396 United-States <=50K
## 397 United-States <=50K
## 398 United-States <=50K
## 399 United-States >50K
## 400 United-States >50K
## 401 United-States <=50K
## 402 United-States <=50K
## 403 United-States >50K
## 404 United-States <=50K
## 405 Puerto-Rico <=50K
## 406 United-States >50K
## 407 United-States <=50K
## 408 United-States <=50K
## 409 United-States >50K
## 410 United-States <=50K
## 411 United-States <=50K
## 412 United-States <=50K
## 413 United-States <=50K
## 414 United-States >50K
## 415 United-States <=50K
## 416 United-States >50K
## 417 Mexico <=50K
## 418 United-States <=50K
## 419 United-States <=50K
## 420 United-States <=50K
## 421 United-States <=50K
## 422 United-States <=50K
## 423 United-States >50K
## 424 United-States <=50K
## 425 United-States <=50K
## 426 United-States <=50K
## 427 United-States <=50K
## 428 Dominican-Republic <=50K
## 429 United-States >50K
## 430 United-States <=50K
## 431 United-States <=50K
## 432 United-States <=50K
## 433 United-States <=50K
## 434 United-States >50K
## 435 Philippines <=50K
## 436 United-States >50K
## 437 United-States <=50K
## 438 United-States <=50K
## 439 United-States <=50K
## 440 United-States <=50K
## 441 United-States <=50K
## 442 United-States <=50K
## 443 United-States <=50K
## 444 United-States >50K
## 445 Mexico <=50K
## 446 United-States <=50K
## 447 United-States <=50K
## 448 United-States <=50K
## 449 United-States <=50K
## 450 United-States <=50K
## 451 United-States <=50K
## 452 United-States >50K
## 453 United-States >50K
## 454 ? >50K
## 455 United-States >50K
## 456 United-States >50K
## 457 United-States <=50K
## 458 United-States <=50K
## 459 United-States <=50K
## 460 United-States <=50K
## 461 United-States <=50K
## 462 United-States <=50K
## 463 United-States <=50K
## 464 United-States <=50K
## 465 United-States <=50K
## 466 United-States <=50K
## 467 United-States <=50K
## 468 United-States <=50K
## 469 United-States >50K
## 470 United-States >50K
## 471 United-States >50K
## 472 United-States <=50K
## 473 United-States <=50K
## 474 United-States <=50K
## 475 United-States <=50K
## 476 United-States <=50K
## 477 United-States <=50K
## 478 United-States <=50K
## 479 United-States <=50K
## 480 United-States <=50K
## 481 Portugal <=50K
## 482 United-States <=50K
## 483 United-States <=50K
## 484 United-States <=50K
## 485 United-States <=50K
## 486 United-States <=50K
## 487 United-States <=50K
## 488 United-States <=50K
## 489 United-States <=50K
## 490 United-States >50K
## 491 United-States <=50K
## 492 United-States <=50K
## 493 United-States <=50K
## 494 United-States <=50K
## 495 United-States <=50K
## 496 United-States <=50K
## 497 United-States <=50K
## 498 El-Salvador <=50K
## 499 United-States <=50K
## 500 United-States <=50K
## 501 Poland <=50K
## 502 United-States <=50K
## 503 United-States <=50K
## 504 France <=50K
## 505 United-States <=50K
## 506 United-States <=50K
## 507 United-States <=50K
## 508 United-States <=50K
## 509 United-States <=50K
## 510 United-States <=50K
## 511 United-States <=50K
## 512 United-States <=50K
## 513 United-States <=50K
## 514 United-States >50K
## 515 United-States <=50K
## 516 United-States <=50K
## 517 United-States <=50K
## 518 United-States <=50K
## 519 United-States <=50K
## 520 United-States >50K
## 521 United-States >50K
## 522 United-States >50K
## 523 United-States <=50K
## 524 United-States >50K
## 525 United-States >50K
## 526 United-States <=50K
## 527 United-States >50K
## 528 United-States <=50K
## 529 United-States <=50K
## 530 United-States <=50K
## 531 Puerto-Rico <=50K
## 532 United-States <=50K
## 533 United-States <=50K
## 534 United-States >50K
## 535 United-States >50K
## 536 United-States <=50K
## 537 United-States <=50K
## 538 United-States <=50K
## 539 United-States <=50K
## 540 United-States <=50K
## 541 United-States <=50K
## 542 United-States <=50K
## 543 United-States <=50K
## 544 United-States <=50K
## 545 United-States <=50K
## 546 United-States <=50K
## 547 United-States <=50K
## 548 United-States <=50K
## 549 United-States <=50K
## 550 United-States <=50K
## 551 United-States <=50K
## 552 United-States <=50K
## 553 United-States <=50K
## 554 United-States <=50K
## 555 United-States >50K
## 556 United-States <=50K
## 557 United-States <=50K
## 558 ? <=50K
## 559 United-States <=50K
## 560 Mexico <=50K
## 561 United-States <=50K
## 562 United-States >50K
## 563 United-States >50K
## 564 United-States <=50K
## 565 United-States >50K
## 566 United-States <=50K
## 567 United-States <=50K
## 568 Mexico <=50K
## 569 United-States <=50K
## 570 United-States <=50K
## 571 United-States <=50K
## 572 United-States <=50K
## 573 United-States <=50K
## 574 United-States >50K
## 575 United-States <=50K
## 576 United-States >50K
## 577 United-States <=50K
## 578 United-States <=50K
## 579 United-States <=50K
## 580 United-States <=50K
## 581 United-States >50K
## 582 United-States >50K
## 583 United-States <=50K
## 584 United-States <=50K
## 585 United-States <=50K
## 586 United-States <=50K
## 587 United-States <=50K
## 588 United-States <=50K
## 589 United-States >50K
## 590 United-States <=50K
## 591 United-States <=50K
## 592 United-States <=50K
## 593 United-States >50K
## 594 Canada >50K
## 595 United-States <=50K
## 596 United-States <=50K
## 597 United-States <=50K
## 598 United-States >50K
## 599 United-States <=50K
## 600 United-States <=50K
## 601 United-States <=50K
## 602 United-States <=50K
## 603 United-States <=50K
## 604 United-States <=50K
## 605 United-States <=50K
## 606 United-States <=50K
## 607 United-States >50K
## 608 United-States <=50K
## 609 United-States <=50K
## 610 United-States <=50K
## 611 United-States <=50K
## 612 United-States <=50K
## 613 United-States <=50K
## 614 United-States <=50K
## 615 United-States <=50K
## 616 United-States <=50K
## 617 United-States <=50K
## 618 United-States <=50K
## 619 United-States <=50K
## 620 United-States >50K
## 621 United-States <=50K
## 622 United-States <=50K
## 623 United-States <=50K
## 624 United-States <=50K
## 625 El-Salvador <=50K
## 626 United-States <=50K
## 627 United-States >50K
## 628 United-States <=50K
## 629 United-States >50K
## 630 United-States >50K
## 631 United-States <=50K
## 632 United-States <=50K
## 633 United-States >50K
## 634 United-States >50K
## 635 United-States <=50K
## 636 United-States <=50K
## 637 United-States <=50K
## 638 United-States >50K
## 639 Cuba <=50K
## 640 United-States >50K
## 641 United-States <=50K
## 642 United-States <=50K
## 643 United-States <=50K
## 644 United-States >50K
## 645 United-States <=50K
## 646 United-States >50K
## 647 United-States <=50K
## 648 United-States <=50K
## 649 United-States <=50K
## 650 United-States >50K
## 651 United-States <=50K
## 652 United-States <=50K
## 653 Taiwan >50K
## 654 United-States <=50K
## 655 United-States <=50K
## 656 Philippines <=50K
## 657 United-States <=50K
## 658 United-States <=50K
## 659 United-States <=50K
## 660 United-States >50K
## 661 United-States >50K
## 662 United-States <=50K
## 663 United-States <=50K
## 664 United-States <=50K
## 665 United-States <=50K
## 666 United-States >50K
## 667 United-States <=50K
## 668 United-States >50K
## 669 South <=50K
## 670 United-States <=50K
## 671 United-States <=50K
## 672 United-States <=50K
## 673 United-States <=50K
## 674 United-States >50K
## 675 United-States >50K
## 676 United-States <=50K
## 677 United-States <=50K
## 678 Mexico <=50K
## 679 United-States <=50K
## 680 United-States <=50K
## 681 United-States <=50K
## 682 Portugal >50K
## 683 United-States >50K
## 684 United-States <=50K
## 685 United-States <=50K
## 686 United-States <=50K
## 687 United-States <=50K
## 688 United-States <=50K
## 689 Dominican-Republic <=50K
## 690 United-States <=50K
## 691 United-States <=50K
## 692 United-States <=50K
## 693 United-States <=50K
## 694 United-States <=50K
## 695 United-States <=50K
## 696 United-States <=50K
## 697 United-States >50K
## 698 United-States >50K
## 699 Poland <=50K
## 700 United-States <=50K
## 701 United-States <=50K
## 702 Cuba <=50K
## 703 United-States <=50K
## 704 United-States <=50K
## 705 United-States >50K
## 706 United-States >50K
## 707 Jamaica <=50K
## 708 United-States <=50K
## 709 United-States <=50K
## 710 United-States <=50K
## 711 United-States <=50K
## 712 United-States <=50K
## 713 ? <=50K
## 714 United-States <=50K
## 715 Honduras <=50K
## 716 United-States <=50K
## 717 United-States <=50K
## 718 United-States <=50K
## 719 United-States <=50K
## 720 United-States >50K
## 721 United-States <=50K
## 722 United-States <=50K
## 723 United-States <=50K
## 724 United-States >50K
## 725 United-States >50K
## 726 ? >50K
## 727 United-States <=50K
## 728 United-States <=50K
## 729 Mexico <=50K
## 730 ? >50K
## 731 United-States <=50K
## 732 United-States <=50K
## 733 United-States <=50K
## 734 United-States <=50K
## 735 United-States <=50K
## 736 United-States <=50K
## 737 United-States <=50K
## 738 United-States <=50K
## 739 United-States <=50K
## 740 United-States >50K
## 741 United-States <=50K
## 742 United-States <=50K
## 743 United-States >50K
## 744 United-States <=50K
## 745 United-States <=50K
## 746 United-States >50K
## 747 United-States >50K
## 748 United-States <=50K
## 749 Haiti <=50K
## 750 United-States <=50K
## 751 United-States >50K
## 752 United-States <=50K
## 753 United-States <=50K
## 754 United-States <=50K
## 755 United-States <=50K
## 756 United-States <=50K
## 757 United-States >50K
## 758 United-States <=50K
## 759 United-States >50K
## 760 United-States <=50K
## 761 United-States <=50K
## 762 United-States >50K
## 763 Portugal <=50K
## 764 United-States >50K
## 765 United-States >50K
## 766 United-States <=50K
## 767 United-States <=50K
## 768 Germany <=50K
## 769 United-States <=50K
## 770 United-States <=50K
## 771 United-States <=50K
## 772 Guatemala <=50K
## 773 United-States <=50K
## 774 United-States >50K
## 775 United-States <=50K
## 776 United-States <=50K
## 777 United-States <=50K
## 778 ? <=50K
## 779 United-States >50K
## 780 United-States <=50K
## 781 ? <=50K
## 782 United-States <=50K
## 783 United-States >50K
## 784 United-States <=50K
## 785 United-States >50K
## 786 United-States <=50K
## 787 United-States <=50K
## 788 United-States <=50K
## 789 United-States <=50K
## 790 United-States <=50K
## 791 United-States >50K
## 792 United-States <=50K
## 793 United-States <=50K
## 794 United-States <=50K
## 795 United-States >50K
## 796 United-States <=50K
## 797 United-States <=50K
## 798 United-States <=50K
## 799 United-States >50K
## 800 England >50K
## 801 United-States <=50K
## 802 United-States <=50K
## 803 United-States <=50K
## 804 United-States <=50K
## 805 United-States <=50K
## 806 United-States >50K
## 807 United-States <=50K
## 808 United-States >50K
## 809 United-States >50K
## 810 United-States <=50K
## 811 United-States <=50K
## 812 United-States <=50K
## 813 United-States <=50K
## 814 United-States <=50K
## 815 United-States <=50K
## 816 Italy <=50K
## 817 United-States <=50K
## 818 United-States >50K
## 819 United-States <=50K
## 820 United-States >50K
## 821 United-States <=50K
## 822 United-States <=50K
## 823 United-States <=50K
## 824 United-States <=50K
## 825 United-States <=50K
## 826 United-States >50K
## 827 United-States <=50K
## 828 United-States <=50K
## 829 United-States <=50K
## 830 United-States <=50K
## 831 United-States <=50K
## 832 United-States >50K
## 833 United-States <=50K
## 834 United-States <=50K
## 835 United-States <=50K
## 836 United-States <=50K
## 837 United-States <=50K
## 838 United-States >50K
## 839 United-States <=50K
## 840 United-States <=50K
## 841 United-States <=50K
## 842 United-States <=50K
## 843 United-States >50K
## 844 United-States <=50K
## 845 United-States >50K
## 846 United-States <=50K
## 847 United-States <=50K
## 848 United-States >50K
## 849 United-States <=50K
## 850 United-States <=50K
## 851 United-States >50K
## 852 United-States <=50K
## 853 United-States <=50K
## 854 United-States <=50K
## 855 United-States >50K
## 856 United-States <=50K
## 857 United-States <=50K
## 858 United-States >50K
## 859 United-States <=50K
## 860 United-States >50K
## 861 United-States <=50K
## 862 Mexico <=50K
## 863 United-States <=50K
## 864 United-States <=50K
## 865 United-States <=50K
## 866 United-States <=50K
## 867 United-States <=50K
## 868 United-States <=50K
## 869 United-States >50K
## 870 United-States <=50K
## 871 United-States >50K
## 872 United-States <=50K
## 873 United-States >50K
## 874 China >50K
## 875 United-States <=50K
## 876 United-States >50K
## 877 United-States <=50K
## 878 United-States <=50K
## 879 United-States >50K
## 880 United-States <=50K
## 881 United-States <=50K
## 882 Philippines >50K
## 883 United-States <=50K
## 884 United-States >50K
## 885 United-States <=50K
## 886 United-States <=50K
## 887 United-States <=50K
## 888 ? <=50K
## 889 United-States <=50K
## 890 United-States <=50K
## 891 United-States <=50K
## 892 United-States <=50K
## 893 India <=50K
## 894 United-States >50K
## 895 United-States >50K
## 896 United-States <=50K
## 897 United-States <=50K
## 898 United-States <=50K
## 899 United-States >50K
## 900 United-States <=50K
## 901 United-States <=50K
## 902 United-States <=50K
## 903 United-States <=50K
## 904 United-States <=50K
## 905 United-States <=50K
## 906 United-States <=50K
## 907 United-States <=50K
## 908 United-States <=50K
## 909 United-States <=50K
## 910 United-States <=50K
## 911 United-States <=50K
## 912 United-States <=50K
## 913 United-States <=50K
## 914 United-States <=50K
## 915 United-States <=50K
## 916 United-States >50K
## 917 South <=50K
## 918 United-States >50K
## 919 United-States <=50K
## 920 United-States <=50K
## 921 United-States <=50K
## 922 United-States <=50K
## 923 Mexico <=50K
## 924 United-States <=50K
## 925 United-States <=50K
## 926 United-States <=50K
## 927 United-States <=50K
## 928 United-States >50K
## 929 United-States <=50K
## 930 Guatemala <=50K
## 931 United-States <=50K
## 932 United-States <=50K
## 933 United-States <=50K
## 934 Mexico <=50K
## 935 United-States >50K
## 936 United-States >50K
## 937 United-States <=50K
## 938 United-States >50K
## 939 United-States <=50K
## 940 United-States >50K
## 941 United-States <=50K
## 942 United-States <=50K
## 943 United-States >50K
## 944 United-States <=50K
## 945 United-States <=50K
## 946 United-States <=50K
## 947 United-States <=50K
## 948 United-States >50K
## 949 United-States <=50K
## 950 United-States <=50K
## 951 United-States >50K
## 952 United-States >50K
## 953 United-States <=50K
## 954 United-States <=50K
## 955 United-States <=50K
## 956 ? <=50K
## 957 United-States >50K
## 958 United-States <=50K
## 959 United-States >50K
## 960 United-States >50K
## 961 United-States <=50K
## 962 United-States <=50K
## 963 Mexico <=50K
## 964 United-States <=50K
## 965 United-States <=50K
## 966 United-States <=50K
## 967 United-States >50K
## 968 United-States <=50K
## 969 India >50K
## 970 United-States <=50K
## 971 United-States >50K
## 972 United-States <=50K
## 973 United-States >50K
## 974 United-States <=50K
## 975 United-States <=50K
## 976 United-States <=50K
## 977 United-States <=50K
## 978 United-States >50K
## 979 United-States <=50K
## 980 United-States <=50K
## 981 United-States <=50K
## 982 United-States <=50K
## 983 United-States <=50K
## 984 United-States <=50K
## 985 United-States >50K
## 986 United-States <=50K
## 987 United-States <=50K
## 988 United-States <=50K
## 989 United-States <=50K
## 990 United-States <=50K
## 991 United-States <=50K
## 992 United-States <=50K
## 993 United-States <=50K
## 994 United-States >50K
## 995 United-States <=50K
## 996 United-States >50K
## 997 United-States <=50K
## 998 United-States >50K
## 999 United-States <=50K
## 1000 United-States >50K
## 1001 United-States <=50K
## 1002 United-States >50K
## 1003 United-States >50K
## 1004 United-States <=50K
## 1005 United-States <=50K
## 1006 United-States <=50K
## 1007 United-States <=50K
## 1008 United-States >50K
## 1009 United-States >50K
## 1010 Germany <=50K
## 1011 United-States <=50K
## 1012 United-States <=50K
## 1013 United-States >50K
## 1014 United-States >50K
## 1015 United-States <=50K
## 1016 United-States <=50K
## 1017 United-States >50K
## 1018 United-States <=50K
## 1019 United-States <=50K
## 1020 United-States <=50K
## 1021 Japan <=50K
## 1022 United-States >50K
## 1023 United-States <=50K
## 1024 United-States <=50K
## 1025 United-States <=50K
## 1026 Yugoslavia <=50K
## 1027 ? <=50K
## 1028 United-States >50K
## 1029 United-States <=50K
## 1030 India <=50K
## 1031 United-States <=50K
## 1032 United-States <=50K
## 1033 United-States <=50K
## 1034 United-States <=50K
## 1035 United-States <=50K
## 1036 United-States <=50K
## 1037 ? <=50K
## 1038 United-States <=50K
## 1039 United-States <=50K
## 1040 United-States <=50K
## 1041 United-States <=50K
## 1042 United-States <=50K
## 1043 United-States <=50K
## 1044 United-States >50K
## 1045 United-States <=50K
## 1046 United-States <=50K
## 1047 United-States <=50K
## 1048 United-States <=50K
## 1049 United-States <=50K
## 1050 United-States >50K
## 1051 United-States <=50K
## 1052 Canada >50K
## 1053 United-States >50K
## 1054 United-States >50K
## 1055 Iran <=50K
## 1056 United-States <=50K
## 1057 United-States <=50K
## 1058 United-States <=50K
## 1059 United-States >50K
## 1060 United-States >50K
## 1061 United-States <=50K
## 1062 United-States <=50K
## 1063 Mexico <=50K
## 1064 United-States <=50K
## 1065 United-States >50K
## 1066 United-States <=50K
## 1067 United-States >50K
## 1068 United-States <=50K
## 1069 United-States <=50K
## 1070 United-States <=50K
## 1071 United-States <=50K
## 1072 United-States >50K
## 1073 United-States >50K
## 1074 United-States >50K
## 1075 United-States <=50K
## 1076 United-States <=50K
## 1077 United-States <=50K
## 1078 Taiwan <=50K
## 1079 United-States <=50K
## 1080 United-States >50K
## 1081 United-States <=50K
## 1082 United-States <=50K
## 1083 United-States <=50K
## 1084 United-States >50K
## 1085 United-States <=50K
## 1086 United-States <=50K
## 1087 United-States <=50K
## 1088 United-States <=50K
## 1089 United-States >50K
## 1090 United-States <=50K
## 1091 United-States >50K
## 1092 United-States >50K
## 1093 China <=50K
## 1094 United-States <=50K
## 1095 United-States <=50K
## 1096 India <=50K
## 1097 United-States <=50K
## 1098 United-States <=50K
## 1099 United-States >50K
## 1100 United-States <=50K
## 1101 United-States <=50K
## 1102 United-States >50K
## 1103 Mexico <=50K
## 1104 United-States <=50K
## 1105 United-States >50K
## 1106 United-States <=50K
## 1107 United-States <=50K
## 1108 United-States <=50K
## 1109 United-States >50K
## 1110 United-States <=50K
## 1111 Mexico >50K
## 1112 United-States >50K
## 1113 United-States <=50K
## 1114 United-States <=50K
## 1115 United-States <=50K
## 1116 ? >50K
## 1117 United-States <=50K
## 1118 United-States <=50K
## 1119 United-States >50K
## 1120 Japan <=50K
## 1121 United-States <=50K
## 1122 United-States <=50K
## 1123 United-States <=50K
## 1124 United-States <=50K
## 1125 United-States <=50K
## 1126 United-States <=50K
## 1127 United-States >50K
## 1128 United-States <=50K
## 1129 United-States <=50K
## 1130 United-States <=50K
## 1131 United-States <=50K
## 1132 United-States <=50K
## 1133 Mexico <=50K
## 1134 United-States <=50K
## 1135 United-States <=50K
## 1136 Jamaica >50K
## 1137 United-States >50K
## 1138 United-States >50K
## 1139 United-States <=50K
## 1140 Mexico <=50K
## 1141 United-States <=50K
## 1142 United-States <=50K
## 1143 Germany <=50K
## 1144 United-States <=50K
## 1145 United-States <=50K
## 1146 United-States >50K
## 1147 United-States <=50K
## 1148 United-States <=50K
## 1149 United-States >50K
## 1150 United-States >50K
## 1151 United-States <=50K
## 1152 United-States <=50K
## 1153 ? <=50K
## 1154 United-States <=50K
## 1155 United-States <=50K
## 1156 Mexico <=50K
## 1157 United-States >50K
## 1158 United-States >50K
## 1159 ? <=50K
## 1160 United-States <=50K
## 1161 United-States <=50K
## 1162 United-States <=50K
## 1163 United-States >50K
## 1164 United-States >50K
## 1165 United-States >50K
## 1166 United-States <=50K
## 1167 United-States >50K
## 1168 United-States <=50K
## 1169 United-States <=50K
## 1170 United-States <=50K
## 1171 United-States <=50K
## 1172 United-States <=50K
## 1173 United-States >50K
## 1174 United-States <=50K
## 1175 United-States <=50K
## 1176 United-States >50K
## 1177 United-States <=50K
## 1178 United-States >50K
## 1179 United-States <=50K
## 1180 United-States <=50K
## 1181 United-States >50K
## 1182 United-States >50K
## 1183 United-States <=50K
## 1184 United-States <=50K
## 1185 United-States <=50K
## 1186 United-States <=50K
## 1187 United-States <=50K
## 1188 United-States <=50K
## 1189 United-States <=50K
## 1190 United-States <=50K
## 1191 United-States <=50K
## 1192 United-States >50K
## 1193 United-States <=50K
## 1194 United-States <=50K
## 1195 United-States <=50K
## 1196 United-States <=50K
## 1197 United-States <=50K
## 1198 United-States <=50K
## 1199 Dominican-Republic <=50K
## 1200 ? <=50K
## 1201 United-States >50K
## 1202 United-States >50K
## 1203 United-States <=50K
## 1204 United-States >50K
## 1205 United-States <=50K
## 1206 United-States >50K
## 1207 United-States <=50K
## 1208 United-States >50K
## 1209 United-States <=50K
## 1210 United-States <=50K
## 1211 United-States >50K
## 1212 United-States <=50K
## 1213 United-States <=50K
## 1214 United-States >50K
## 1215 United-States <=50K
## 1216 United-States >50K
## 1217 United-States >50K
## 1218 China <=50K
## 1219 United-States <=50K
## 1220 United-States >50K
## 1221 United-States >50K
## 1222 United-States <=50K
## 1223 United-States <=50K
## 1224 United-States <=50K
## 1225 ? >50K
## 1226 United-States <=50K
## 1227 United-States >50K
## 1228 United-States <=50K
## 1229 England <=50K
## 1230 United-States <=50K
## 1231 United-States >50K
## 1232 United-States <=50K
## 1233 United-States <=50K
## 1234 United-States <=50K
## 1235 United-States <=50K
## 1236 United-States <=50K
## 1237 United-States <=50K
## 1238 Cuba <=50K
## 1239 United-States <=50K
## 1240 United-States <=50K
## 1241 United-States >50K
## 1242 United-States <=50K
## 1243 United-States <=50K
## 1244 United-States >50K
## 1245 United-States <=50K
## 1246 United-States <=50K
## 1247 United-States >50K
## 1248 United-States <=50K
## 1249 United-States <=50K
## 1250 United-States <=50K
## 1251 United-States >50K
## 1252 United-States <=50K
## 1253 ? <=50K
## 1254 United-States <=50K
## 1255 United-States <=50K
## 1256 United-States <=50K
## 1257 United-States <=50K
## 1258 United-States >50K
## 1259 United-States >50K
## 1260 United-States <=50K
## 1261 United-States <=50K
## 1262 United-States >50K
## 1263 United-States <=50K
## 1264 United-States <=50K
## 1265 United-States <=50K
## 1266 United-States <=50K
## 1267 United-States >50K
## 1268 United-States <=50K
## 1269 United-States <=50K
## 1270 United-States <=50K
## 1271 United-States <=50K
## 1272 United-States <=50K
## 1273 United-States <=50K
## 1274 United-States <=50K
## 1275 United-States <=50K
## 1276 United-States >50K
## 1277 United-States >50K
## 1278 United-States >50K
## 1279 United-States <=50K
## 1280 United-States <=50K
## 1281 United-States <=50K
## 1282 Philippines >50K
## 1283 United-States >50K
## 1284 United-States <=50K
## 1285 United-States >50K
## 1286 United-States <=50K
## 1287 United-States >50K
## 1288 United-States <=50K
## 1289 United-States <=50K
## 1290 United-States <=50K
## 1291 United-States <=50K
## 1292 El-Salvador <=50K
## 1293 United-States <=50K
## 1294 United-States <=50K
## 1295 United-States <=50K
## 1296 United-States <=50K
## 1297 United-States <=50K
## 1298 United-States <=50K
## 1299 United-States <=50K
## 1300 United-States <=50K
## 1301 United-States <=50K
## 1302 Japan <=50K
## 1303 United-States >50K
## 1304 United-States <=50K
## 1305 United-States <=50K
## 1306 United-States >50K
## 1307 United-States <=50K
## 1308 Mexico <=50K
## 1309 United-States <=50K
## 1310 United-States >50K
## 1311 United-States <=50K
## 1312 United-States <=50K
## 1313 United-States <=50K
## 1314 United-States <=50K
## 1315 Canada >50K
## 1316 United-States <=50K
## 1317 United-States >50K
## 1318 United-States <=50K
## 1319 United-States >50K
## 1320 United-States >50K
## 1321 United-States <=50K
## 1322 United-States <=50K
## 1323 United-States >50K
## 1324 United-States >50K
## 1325 United-States >50K
## 1326 United-States <=50K
## 1327 ? <=50K
## 1328 India >50K
## 1329 United-States <=50K
## 1330 United-States <=50K
## 1331 United-States <=50K
## 1332 United-States <=50K
## 1333 United-States <=50K
## 1334 United-States <=50K
## 1335 United-States >50K
## 1336 United-States <=50K
## 1337 United-States <=50K
## 1338 United-States >50K
## 1339 United-States <=50K
## 1340 United-States <=50K
## 1341 United-States <=50K
## 1342 United-States <=50K
## 1343 United-States <=50K
## 1344 United-States <=50K
## 1345 United-States <=50K
## 1346 United-States <=50K
## 1347 Germany >50K
## 1348 United-States <=50K
## 1349 ? >50K
## 1350 United-States <=50K
## 1351 United-States >50K
## 1352 United-States >50K
## 1353 United-States <=50K
## 1354 United-States <=50K
## 1355 United-States <=50K
## 1356 United-States >50K
## 1357 United-States <=50K
## 1358 United-States <=50K
## 1359 United-States <=50K
## 1360 United-States <=50K
## 1361 United-States <=50K
## 1362 United-States >50K
## 1363 United-States <=50K
## 1364 Italy <=50K
## 1365 United-States <=50K
## 1366 United-States <=50K
## 1367 United-States <=50K
## 1368 United-States <=50K
## 1369 Japan >50K
## 1370 United-States <=50K
## 1371 United-States <=50K
## 1372 United-States <=50K
## 1373 Poland <=50K
## 1374 United-States <=50K
## 1375 United-States <=50K
## 1376 United-States <=50K
## 1377 United-States <=50K
## 1378 United-States <=50K
## 1379 United-States >50K
## 1380 Canada <=50K
## 1381 United-States <=50K
## 1382 United-States <=50K
## 1383 United-States <=50K
## 1384 United-States <=50K
## 1385 United-States <=50K
## 1386 United-States <=50K
## 1387 United-States <=50K
## 1388 Iran >50K
## 1389 United-States <=50K
## 1390 United-States <=50K
## 1391 United-States <=50K
## 1392 ? >50K
## 1393 United-States <=50K
## 1394 United-States <=50K
## 1395 United-States <=50K
## 1396 United-States >50K
## 1397 United-States <=50K
## 1398 United-States <=50K
## 1399 United-States <=50K
## 1400 United-States >50K
## 1401 United-States <=50K
## 1402 United-States <=50K
## 1403 United-States <=50K
## 1404 United-States <=50K
## 1405 United-States <=50K
## 1406 United-States >50K
## 1407 United-States <=50K
## 1408 United-States >50K
## 1409 United-States <=50K
## 1410 United-States <=50K
## 1411 United-States <=50K
## 1412 Thailand <=50K
## 1413 United-States >50K
## 1414 United-States >50K
## 1415 United-States <=50K
## 1416 United-States <=50K
## 1417 United-States <=50K
## 1418 United-States >50K
## 1419 United-States >50K
## 1420 United-States <=50K
## 1421 United-States <=50K
## 1422 United-States <=50K
## 1423 United-States <=50K
## 1424 United-States <=50K
## 1425 United-States <=50K
## 1426 Germany <=50K
## 1427 United-States >50K
## 1428 United-States <=50K
## 1429 United-States <=50K
## 1430 United-States >50K
## 1431 Peru <=50K
## 1432 United-States <=50K
## 1433 United-States >50K
## 1434 United-States <=50K
## 1435 United-States <=50K
## 1436 United-States <=50K
## 1437 Mexico <=50K
## 1438 United-States >50K
## 1439 United-States <=50K
## 1440 United-States >50K
## 1441 United-States <=50K
## 1442 United-States <=50K
## 1443 United-States >50K
## 1444 United-States <=50K
## 1445 United-States >50K
## 1446 United-States <=50K
## 1447 United-States <=50K
## 1448 United-States <=50K
## 1449 United-States >50K
## 1450 United-States >50K
## 1451 United-States <=50K
## 1452 United-States <=50K
## 1453 United-States <=50K
## 1454 United-States >50K
## 1455 United-States >50K
## 1456 United-States <=50K
## 1457 United-States <=50K
## 1458 United-States <=50K
## 1459 United-States <=50K
## 1460 United-States >50K
## 1461 United-States >50K
## 1462 United-States <=50K
## 1463 United-States >50K
## 1464 United-States >50K
## 1465 United-States <=50K
## 1466 United-States <=50K
## 1467 United-States <=50K
## 1468 United-States <=50K
## 1469 United-States <=50K
## 1470 United-States >50K
## 1471 United-States >50K
## 1472 United-States <=50K
## 1473 United-States <=50K
## 1474 United-States <=50K
## 1475 United-States <=50K
## 1476 United-States <=50K
## 1477 United-States <=50K
## 1478 United-States <=50K
## 1479 United-States <=50K
## 1480 United-States <=50K
## 1481 United-States <=50K
## 1482 United-States >50K
## 1483 United-States >50K
## 1484 United-States <=50K
## 1485 United-States >50K
## 1486 Mexico <=50K
## 1487 United-States <=50K
## 1488 United-States <=50K
## 1489 United-States <=50K
## 1490 United-States >50K
## 1491 United-States <=50K
## 1492 United-States <=50K
## 1493 United-States >50K
## 1494 United-States >50K
## 1495 United-States <=50K
## 1496 United-States >50K
## 1497 United-States >50K
## 1498 United-States <=50K
## 1499 United-States >50K
## 1500 United-States <=50K
## 1501 United-States >50K
## 1502 United-States <=50K
## 1503 United-States <=50K
## 1504 United-States >50K
## 1505 United-States <=50K
## 1506 United-States <=50K
## 1507 United-States <=50K
## 1508 United-States <=50K
## 1509 United-States <=50K
## 1510 United-States <=50K
## 1511 United-States <=50K
## 1512 United-States >50K
## 1513 United-States >50K
## 1514 United-States <=50K
## 1515 England >50K
## 1516 China <=50K
## 1517 United-States >50K
## 1518 United-States <=50K
## 1519 United-States <=50K
## 1520 United-States <=50K
## 1521 United-States <=50K
## 1522 United-States <=50K
## 1523 United-States <=50K
## 1524 United-States <=50K
## 1525 United-States <=50K
## 1526 United-States <=50K
## 1527 United-States <=50K
## 1528 United-States <=50K
## 1529 United-States >50K
## 1530 United-States <=50K
## 1531 United-States <=50K
## 1532 United-States >50K
## 1533 United-States >50K
## 1534 United-States >50K
## 1535 United-States <=50K
## 1536 United-States <=50K
## 1537 United-States <=50K
## 1538 United-States <=50K
## 1539 United-States <=50K
## 1540 United-States <=50K
## 1541 United-States <=50K
## 1542 United-States <=50K
## 1543 United-States <=50K
## 1544 United-States <=50K
## 1545 United-States <=50K
## 1546 United-States <=50K
## 1547 United-States <=50K
## 1548 United-States <=50K
## 1549 United-States >50K
## 1550 United-States <=50K
## 1551 United-States <=50K
## 1552 United-States <=50K
## 1553 United-States <=50K
## 1554 United-States <=50K
## 1555 ? >50K
## 1556 United-States <=50K
## 1557 United-States <=50K
## 1558 ? <=50K
## 1559 United-States <=50K
## 1560 United-States <=50K
## 1561 United-States <=50K
## 1562 United-States <=50K
## 1563 United-States >50K
## 1564 United-States <=50K
## 1565 United-States <=50K
## 1566 Outlying-US(Guam-USVI-etc) <=50K
## 1567 United-States <=50K
## 1568 United-States <=50K
## 1569 United-States <=50K
## 1570 United-States <=50K
## 1571 Jamaica <=50K
## 1572 United-States >50K
## 1573 United-States <=50K
## 1574 United-States <=50K
## 1575 United-States <=50K
## 1576 United-States <=50K
## 1577 United-States <=50K
## 1578 United-States <=50K
## 1579 United-States <=50K
## 1580 United-States <=50K
## 1581 United-States >50K
## 1582 ? <=50K
## 1583 United-States <=50K
## 1584 United-States >50K
## 1585 United-States <=50K
## 1586 United-States >50K
## 1587 Scotland <=50K
## 1588 United-States <=50K
## 1589 United-States <=50K
## 1590 United-States <=50K
## 1591 United-States <=50K
## 1592 United-States <=50K
## 1593 United-States <=50K
## 1594 ? >50K
## 1595 United-States <=50K
## 1596 United-States >50K
## 1597 United-States <=50K
## 1598 United-States <=50K
## 1599 United-States <=50K
## 1600 United-States <=50K
## 1601 United-States <=50K
## 1602 United-States <=50K
## 1603 United-States <=50K
## 1604 United-States >50K
## 1605 United-States <=50K
## 1606 United-States <=50K
## 1607 United-States <=50K
## 1608 United-States <=50K
## 1609 United-States <=50K
## 1610 Philippines <=50K
## 1611 United-States <=50K
## 1612 United-States >50K
## 1613 England >50K
## 1614 United-States >50K
## 1615 United-States <=50K
## 1616 United-States >50K
## 1617 United-States >50K
## 1618 United-States <=50K
## 1619 United-States <=50K
## 1620 United-States <=50K
## 1621 United-States >50K
## 1622 United-States >50K
## 1623 Philippines <=50K
## 1624 United-States <=50K
## 1625 United-States <=50K
## 1626 United-States >50K
## 1627 United-States <=50K
## 1628 United-States >50K
## 1629 United-States <=50K
## 1630 Haiti <=50K
## 1631 Mexico <=50K
## 1632 United-States <=50K
## 1633 United-States >50K
## 1634 Mexico <=50K
## 1635 Mexico <=50K
## 1636 United-States >50K
## 1637 United-States <=50K
## 1638 United-States <=50K
## 1639 United-States >50K
## 1640 United-States >50K
## 1641 United-States <=50K
## 1642 United-States >50K
## 1643 United-States >50K
## 1644 United-States >50K
## 1645 United-States <=50K
## 1646 United-States >50K
## 1647 United-States <=50K
## 1648 United-States <=50K
## 1649 United-States >50K
## 1650 United-States <=50K
## 1651 United-States <=50K
## 1652 United-States <=50K
## 1653 United-States <=50K
## 1654 United-States <=50K
## 1655 United-States >50K
## 1656 Italy >50K
## 1657 United-States <=50K
## 1658 United-States <=50K
## 1659 United-States <=50K
## 1660 United-States <=50K
## 1661 United-States <=50K
## 1662 United-States >50K
## 1663 United-States <=50K
## 1664 Cuba <=50K
## 1665 United-States <=50K
## 1666 United-States <=50K
## 1667 United-States >50K
## 1668 United-States <=50K
## 1669 United-States <=50K
## 1670 United-States >50K
## 1671 United-States >50K
## 1672 United-States <=50K
## 1673 United-States <=50K
## 1674 United-States <=50K
## 1675 Italy >50K
## 1676 United-States <=50K
## 1677 ? >50K
## 1678 United-States <=50K
## 1679 United-States <=50K
## 1680 United-States <=50K
## 1681 United-States <=50K
## 1682 United-States <=50K
## 1683 United-States >50K
## 1684 United-States <=50K
## 1685 United-States <=50K
## 1686 United-States >50K
## 1687 United-States <=50K
## 1688 United-States >50K
## 1689 Canada <=50K
## 1690 United-States >50K
## 1691 United-States <=50K
## 1692 United-States <=50K
## 1693 United-States <=50K
## 1694 United-States >50K
## 1695 United-States <=50K
## 1696 United-States >50K
## 1697 United-States <=50K
## 1698 United-States <=50K
## 1699 United-States <=50K
## 1700 United-States <=50K
## 1701 United-States <=50K
## 1702 United-States <=50K
## 1703 United-States <=50K
## 1704 United-States <=50K
## 1705 United-States <=50K
## 1706 United-States <=50K
## 1707 United-States >50K
## 1708 United-States <=50K
## 1709 China <=50K
## 1710 United-States >50K
## 1711 United-States <=50K
## 1712 ? <=50K
## 1713 United-States <=50K
## 1714 United-States >50K
## 1715 United-States <=50K
## 1716 United-States <=50K
## 1717 United-States <=50K
## 1718 China >50K
## 1719 United-States >50K
## 1720 United-States <=50K
## 1721 United-States <=50K
## 1722 United-States <=50K
## 1723 United-States >50K
## 1724 United-States >50K
## 1725 United-States <=50K
## 1726 United-States <=50K
## 1727 United-States >50K
## 1728 United-States <=50K
## 1729 United-States >50K
## 1730 United-States <=50K
## 1731 United-States <=50K
## 1732 United-States <=50K
## 1733 United-States <=50K
## 1734 United-States <=50K
## 1735 United-States <=50K
## 1736 United-States >50K
## 1737 United-States <=50K
## 1738 United-States <=50K
## 1739 ? >50K
## 1740 United-States <=50K
## 1741 United-States >50K
## 1742 United-States <=50K
## 1743 United-States <=50K
## 1744 United-States <=50K
## 1745 United-States <=50K
## 1746 United-States <=50K
## 1747 United-States <=50K
## 1748 United-States <=50K
## 1749 United-States <=50K
## 1750 United-States <=50K
## 1751 United-States <=50K
## 1752 United-States <=50K
## 1753 United-States <=50K
## 1754 United-States <=50K
## 1755 United-States >50K
## 1756 United-States <=50K
## 1757 Japan <=50K
## 1758 United-States <=50K
## 1759 United-States <=50K
## 1760 United-States <=50K
## 1761 United-States >50K
## 1762 United-States <=50K
## 1763 United-States <=50K
## 1764 United-States <=50K
## 1765 United-States >50K
## 1766 United-States >50K
## 1767 United-States <=50K
## 1768 United-States <=50K
## 1769 United-States <=50K
## 1770 United-States >50K
## 1771 United-States <=50K
## 1772 United-States >50K
## 1773 United-States >50K
## 1774 United-States <=50K
## 1775 United-States >50K
## 1776 United-States <=50K
## 1777 United-States <=50K
## 1778 United-States <=50K
## 1779 United-States <=50K
## 1780 United-States >50K
## 1781 United-States >50K
## 1782 United-States <=50K
## 1783 United-States <=50K
## 1784 United-States >50K
## 1785 United-States <=50K
## 1786 United-States >50K
## 1787 United-States >50K
## 1788 United-States >50K
## 1789 United-States <=50K
## 1790 Puerto-Rico <=50K
## 1791 United-States <=50K
## 1792 United-States <=50K
## 1793 United-States <=50K
## 1794 Philippines <=50K
## 1795 Trinadad&Tobago <=50K
## 1796 United-States >50K
## 1797 United-States <=50K
## 1798 United-States <=50K
## 1799 United-States >50K
## 1800 United-States <=50K
## 1801 United-States <=50K
## 1802 United-States <=50K
## 1803 United-States <=50K
## 1804 United-States >50K
## 1805 United-States <=50K
## 1806 United-States <=50K
## 1807 United-States <=50K
## 1808 United-States <=50K
## 1809 United-States <=50K
## 1810 United-States <=50K
## 1811 United-States <=50K
## 1812 United-States >50K
## 1813 United-States >50K
## 1814 United-States <=50K
## 1815 United-States >50K
## 1816 United-States <=50K
## 1817 United-States <=50K
## 1818 United-States <=50K
## 1819 ? <=50K
## 1820 United-States >50K
## 1821 Mexico <=50K
## 1822 United-States <=50K
## 1823 United-States <=50K
## 1824 United-States <=50K
## 1825 United-States >50K
## 1826 United-States >50K
## 1827 Dominican-Republic >50K
## 1828 United-States <=50K
## 1829 United-States <=50K
## 1830 United-States <=50K
## 1831 United-States <=50K
## 1832 United-States <=50K
## 1833 United-States <=50K
## 1834 United-States <=50K
## 1835 United-States <=50K
## 1836 United-States <=50K
## 1837 United-States <=50K
## 1838 United-States <=50K
## 1839 United-States <=50K
## 1840 Philippines <=50K
## 1841 United-States <=50K
## 1842 United-States <=50K
## 1843 United-States <=50K
## 1844 United-States <=50K
## 1845 United-States <=50K
## 1846 United-States <=50K
## 1847 United-States <=50K
## 1848 Puerto-Rico <=50K
## 1849 United-States <=50K
## 1850 United-States >50K
## 1851 United-States <=50K
## 1852 United-States <=50K
## 1853 United-States <=50K
## 1854 United-States <=50K
## 1855 United-States <=50K
## 1856 United-States <=50K
## 1857 United-States <=50K
## 1858 United-States <=50K
## 1859 United-States <=50K
## 1860 United-States >50K
## 1861 United-States <=50K
## 1862 United-States <=50K
## 1863 United-States >50K
## 1864 United-States <=50K
## 1865 United-States >50K
## 1866 United-States <=50K
## 1867 United-States <=50K
## 1868 United-States <=50K
## 1869 United-States <=50K
## 1870 United-States >50K
## 1871 United-States <=50K
## 1872 United-States <=50K
## 1873 United-States >50K
## 1874 United-States <=50K
## 1875 United-States <=50K
## 1876 United-States >50K
## 1877 United-States >50K
## 1878 United-States <=50K
## 1879 United-States <=50K
## 1880 United-States <=50K
## 1881 El-Salvador <=50K
## 1882 United-States >50K
## 1883 Italy >50K
## 1884 United-States <=50K
## 1885 United-States <=50K
## 1886 United-States <=50K
## 1887 United-States <=50K
## 1888 United-States >50K
## 1889 Greece <=50K
## 1890 United-States <=50K
## 1891 United-States <=50K
## 1892 United-States <=50K
## 1893 United-States <=50K
## 1894 United-States >50K
## 1895 United-States <=50K
## 1896 United-States >50K
## 1897 United-States <=50K
## 1898 United-States <=50K
## 1899 United-States <=50K
## 1900 United-States <=50K
## 1901 ? <=50K
## 1902 United-States <=50K
## 1903 United-States <=50K
## 1904 United-States <=50K
## 1905 United-States >50K
## 1906 United-States >50K
## 1907 United-States <=50K
## 1908 United-States <=50K
## 1909 United-States <=50K
## 1910 United-States <=50K
## 1911 United-States <=50K
## 1912 United-States <=50K
## 1913 United-States <=50K
## 1914 Mexico <=50K
## 1915 United-States <=50K
## 1916 United-States >50K
## 1917 United-States <=50K
## 1918 United-States <=50K
## 1919 United-States <=50K
## 1920 United-States <=50K
## 1921 United-States <=50K
## 1922 United-States >50K
## 1923 United-States >50K
## 1924 United-States <=50K
## 1925 United-States <=50K
## 1926 United-States >50K
## 1927 United-States <=50K
## 1928 United-States <=50K
## 1929 United-States <=50K
## 1930 United-States <=50K
## 1931 United-States <=50K
## 1932 United-States <=50K
## 1933 China <=50K
## 1934 United-States >50K
## 1935 United-States <=50K
## 1936 United-States <=50K
## 1937 United-States <=50K
## 1938 United-States <=50K
## 1939 Canada >50K
## 1940 United-States <=50K
## 1941 United-States <=50K
## 1942 United-States <=50K
## 1943 United-States <=50K
## 1944 United-States >50K
## 1945 United-States <=50K
## 1946 United-States >50K
## 1947 United-States <=50K
## 1948 United-States <=50K
## 1949 Nicaragua <=50K
## 1950 United-States >50K
## 1951 United-States <=50K
## 1952 United-States <=50K
## 1953 United-States <=50K
## 1954 United-States <=50K
## 1955 United-States <=50K
## 1956 United-States <=50K
## 1957 United-States <=50K
## 1958 United-States <=50K
## 1959 United-States <=50K
## 1960 United-States >50K
## 1961 United-States <=50K
## 1962 United-States >50K
## 1963 United-States >50K
## 1964 United-States >50K
## 1965 United-States <=50K
## 1966 United-States <=50K
## 1967 United-States <=50K
## 1968 United-States <=50K
## 1969 United-States <=50K
## 1970 South >50K
## 1971 United-States <=50K
## 1972 United-States <=50K
## 1973 United-States >50K
## 1974 Germany <=50K
## 1975 United-States <=50K
## 1976 Cambodia >50K
## 1977 United-States <=50K
## 1978 United-States <=50K
## 1979 United-States >50K
## 1980 United-States <=50K
## 1981 United-States <=50K
## 1982 United-States >50K
## 1983 United-States <=50K
## 1984 United-States >50K
## 1985 United-States >50K
## 1986 United-States <=50K
## 1987 United-States >50K
## 1988 United-States <=50K
## 1989 United-States <=50K
## 1990 United-States <=50K
## 1991 ? <=50K
## 1992 United-States <=50K
## 1993 United-States >50K
## 1994 United-States <=50K
## 1995 United-States >50K
## 1996 United-States <=50K
## 1997 United-States >50K
## 1998 United-States <=50K
## 1999 United-States >50K
## 2000 United-States <=50K
## 2001 United-States <=50K
## 2002 United-States <=50K
## 2003 United-States <=50K
## 2004 United-States >50K
## 2005 United-States <=50K
## 2006 United-States <=50K
## 2007 Jamaica <=50K
## 2008 United-States <=50K
## 2009 United-States <=50K
## 2010 United-States <=50K
## 2011 United-States <=50K
## 2012 United-States <=50K
## 2013 United-States <=50K
## 2014 United-States <=50K
## 2015 United-States <=50K
## 2016 ? <=50K
## 2017 United-States <=50K
## 2018 United-States <=50K
## 2019 Cuba >50K
## 2020 United-States <=50K
## 2021 United-States <=50K
## 2022 England <=50K
## 2023 Nicaragua <=50K
## 2024 United-States <=50K
## 2025 United-States <=50K
## 2026 United-States <=50K
## 2027 United-States >50K
## 2028 United-States <=50K
## 2029 United-States <=50K
## 2030 United-States <=50K
## 2031 United-States >50K
## 2032 United-States >50K
## 2033 United-States <=50K
## 2034 United-States <=50K
## 2035 United-States <=50K
## 2036 United-States <=50K
## 2037 United-States <=50K
## 2038 United-States <=50K
## 2039 United-States <=50K
## 2040 United-States >50K
## 2041 United-States <=50K
## 2042 United-States <=50K
## 2043 United-States <=50K
## 2044 United-States >50K
## 2045 United-States <=50K
## 2046 United-States <=50K
## 2047 United-States <=50K
## 2048 United-States <=50K
## 2049 United-States >50K
## 2050 United-States <=50K
## 2051 United-States <=50K
## 2052 United-States >50K
## 2053 United-States <=50K
## 2054 Mexico <=50K
## 2055 United-States <=50K
## 2056 United-States <=50K
## 2057 United-States >50K
## 2058 United-States <=50K
## 2059 United-States <=50K
## 2060 Mexico <=50K
## 2061 United-States <=50K
## 2062 United-States <=50K
## 2063 United-States <=50K
## 2064 United-States >50K
## 2065 United-States <=50K
## 2066 Mexico <=50K
## 2067 United-States >50K
## 2068 United-States <=50K
## 2069 United-States >50K
## 2070 United-States <=50K
## 2071 United-States <=50K
## 2072 United-States <=50K
## 2073 United-States <=50K
## 2074 United-States >50K
## 2075 United-States >50K
## 2076 United-States <=50K
## 2077 United-States <=50K
## 2078 United-States <=50K
## 2079 United-States <=50K
## 2080 United-States <=50K
## 2081 United-States <=50K
## 2082 United-States >50K
## 2083 United-States <=50K
## 2084 United-States <=50K
## 2085 Honduras <=50K
## 2086 United-States >50K
## 2087 United-States >50K
## 2088 United-States <=50K
## 2089 United-States >50K
## 2090 United-States >50K
## 2091 United-States >50K
## 2092 United-States <=50K
## 2093 United-States <=50K
## 2094 United-States >50K
## 2095 United-States <=50K
## 2096 United-States >50K
## 2097 Japan <=50K
## 2098 United-States <=50K
## 2099 United-States >50K
## 2100 ? <=50K
## 2101 United-States >50K
## 2102 United-States <=50K
## 2103 United-States <=50K
## 2104 United-States >50K
## 2105 ? <=50K
## 2106 United-States <=50K
## 2107 United-States >50K
## 2108 Dominican-Republic <=50K
## 2109 United-States <=50K
## 2110 United-States >50K
## 2111 United-States >50K
## 2112 United-States <=50K
## 2113 United-States >50K
## 2114 United-States <=50K
## 2115 United-States <=50K
## 2116 United-States <=50K
## 2117 United-States <=50K
## 2118 United-States <=50K
## 2119 United-States <=50K
## 2120 United-States <=50K
## 2121 United-States <=50K
## 2122 United-States <=50K
## 2123 United-States <=50K
## 2124 United-States <=50K
## 2125 El-Salvador <=50K
## 2126 United-States <=50K
## 2127 United-States <=50K
## 2128 United-States <=50K
## 2129 Jamaica >50K
## 2130 United-States <=50K
## 2131 India <=50K
## 2132 United-States <=50K
## 2133 United-States <=50K
## 2134 United-States <=50K
## 2135 United-States <=50K
## 2136 United-States <=50K
## 2137 United-States <=50K
## 2138 Puerto-Rico <=50K
## 2139 United-States <=50K
## 2140 United-States >50K
## 2141 United-States <=50K
## 2142 United-States <=50K
## 2143 United-States <=50K
## 2144 United-States <=50K
## 2145 Puerto-Rico <=50K
## 2146 United-States <=50K
## 2147 United-States <=50K
## 2148 United-States <=50K
## 2149 United-States <=50K
## 2150 United-States <=50K
## 2151 United-States <=50K
## 2152 United-States <=50K
## 2153 United-States <=50K
## 2154 United-States >50K
## 2155 United-States <=50K
## 2156 United-States <=50K
## 2157 United-States <=50K
## 2158 United-States >50K
## 2159 United-States >50K
## 2160 Italy >50K
## 2161 United-States >50K
## 2162 United-States <=50K
## 2163 United-States <=50K
## 2164 United-States <=50K
## 2165 United-States >50K
## 2166 United-States <=50K
## 2167 United-States <=50K
## 2168 United-States >50K
## 2169 United-States <=50K
## 2170 United-States <=50K
## 2171 United-States <=50K
## 2172 United-States <=50K
## 2173 United-States <=50K
## 2174 United-States <=50K
## 2175 United-States <=50K
## 2176 United-States >50K
## 2177 United-States <=50K
## 2178 United-States <=50K
## 2179 United-States >50K
## 2180 United-States >50K
## 2181 United-States >50K
## 2182 ? >50K
## 2183 United-States >50K
## 2184 United-States <=50K
## 2185 United-States >50K
## 2186 United-States <=50K
## 2187 United-States <=50K
## 2188 United-States <=50K
## 2189 United-States <=50K
## 2190 United-States <=50K
## 2191 United-States >50K
## 2192 United-States <=50K
## 2193 United-States <=50K
## 2194 Mexico <=50K
## 2195 United-States <=50K
## 2196 United-States <=50K
## 2197 United-States >50K
## 2198 United-States <=50K
## 2199 United-States >50K
## 2200 United-States <=50K
## 2201 United-States <=50K
## 2202 United-States <=50K
## 2203 United-States <=50K
## 2204 United-States <=50K
## 2205 United-States <=50K
## 2206 United-States >50K
## 2207 United-States >50K
## 2208 United-States <=50K
## 2209 South <=50K
## 2210 United-States <=50K
## 2211 United-States <=50K
## 2212 United-States <=50K
## 2213 United-States <=50K
## 2214 United-States >50K
## 2215 United-States <=50K
## 2216 United-States >50K
## 2217 United-States <=50K
## 2218 United-States >50K
## 2219 United-States <=50K
## 2220 Mexico <=50K
## 2221 United-States <=50K
## 2222 United-States <=50K
## 2223 United-States <=50K
## 2224 United-States <=50K
## 2225 United-States <=50K
## 2226 United-States <=50K
## 2227 United-States <=50K
## 2228 United-States >50K
## 2229 United-States >50K
## 2230 Cuba <=50K
## 2231 United-States >50K
## 2232 United-States <=50K
## 2233 United-States <=50K
## 2234 Germany >50K
## 2235 United-States <=50K
## 2236 United-States <=50K
## 2237 United-States >50K
## 2238 United-States >50K
## 2239 United-States <=50K
## 2240 United-States <=50K
## 2241 United-States >50K
## 2242 United-States <=50K
## 2243 United-States >50K
## 2244 United-States >50K
## 2245 United-States <=50K
## 2246 United-States >50K
## 2247 United-States <=50K
## 2248 United-States <=50K
## 2249 United-States <=50K
## 2250 Philippines >50K
## 2251 United-States <=50K
## 2252 United-States <=50K
## 2253 United-States >50K
## 2254 United-States <=50K
## 2255 United-States >50K
## 2256 United-States <=50K
## 2257 United-States <=50K
## 2258 United-States <=50K
## 2259 United-States >50K
## 2260 Vietnam <=50K
## 2261 United-States <=50K
## 2262 United-States <=50K
## 2263 United-States <=50K
## 2264 United-States <=50K
## 2265 United-States <=50K
## 2266 United-States >50K
## 2267 Mexico <=50K
## 2268 United-States <=50K
## 2269 Poland <=50K
## 2270 United-States <=50K
## 2271 United-States <=50K
## 2272 United-States <=50K
## 2273 United-States <=50K
## 2274 United-States <=50K
## 2275 United-States <=50K
## 2276 United-States <=50K
## 2277 United-States <=50K
## 2278 United-States <=50K
## 2279 United-States <=50K
## 2280 United-States <=50K
## 2281 United-States <=50K
## 2282 United-States <=50K
## 2283 Mexico >50K
## 2284 United-States <=50K
## 2285 Canada <=50K
## 2286 United-States <=50K
## 2287 Taiwan >50K
## 2288 United-States >50K
## 2289 Haiti <=50K
## 2290 United-States <=50K
## 2291 United-States <=50K
## 2292 United-States <=50K
## 2293 United-States <=50K
## 2294 United-States >50K
## 2295 United-States <=50K
## 2296 United-States <=50K
## 2297 United-States <=50K
## 2298 United-States <=50K
## 2299 United-States <=50K
## 2300 United-States <=50K
## 2301 United-States <=50K
## 2302 United-States <=50K
## 2303 United-States <=50K
## 2304 United-States <=50K
## 2305 United-States >50K
## 2306 United-States >50K
## 2307 United-States <=50K
## 2308 United-States <=50K
## 2309 United-States >50K
## 2310 United-States <=50K
## 2311 United-States <=50K
## 2312 United-States >50K
## 2313 United-States >50K
## 2314 United-States <=50K
## 2315 United-States <=50K
## 2316 United-States <=50K
## 2317 United-States <=50K
## 2318 United-States <=50K
## 2319 United-States >50K
## 2320 United-States >50K
## 2321 United-States <=50K
## 2322 United-States >50K
## 2323 United-States <=50K
## 2324 China <=50K
## 2325 United-States >50K
## 2326 United-States >50K
## 2327 United-States <=50K
## 2328 Canada <=50K
## 2329 United-States <=50K
## 2330 United-States <=50K
## 2331 Philippines >50K
## 2332 United-States <=50K
## 2333 United-States >50K
## 2334 United-States <=50K
## 2335 United-States >50K
## 2336 United-States <=50K
## 2337 Guatemala <=50K
## 2338 United-States <=50K
## 2339 United-States <=50K
## 2340 Taiwan >50K
## 2341 United-States <=50K
## 2342 Canada <=50K
## 2343 Mexico <=50K
## 2344 United-States <=50K
## 2345 United-States >50K
## 2346 United-States >50K
## 2347 United-States <=50K
## 2348 United-States <=50K
## 2349 United-States >50K
## 2350 United-States <=50K
## 2351 United-States <=50K
## 2352 United-States >50K
## 2353 United-States >50K
## 2354 England <=50K
## 2355 United-States <=50K
## 2356 United-States <=50K
## 2357 United-States <=50K
## 2358 United-States >50K
## 2359 United-States <=50K
## 2360 United-States <=50K
## 2361 United-States <=50K
## 2362 United-States >50K
## 2363 United-States >50K
## 2364 United-States <=50K
## 2365 United-States <=50K
## 2366 United-States <=50K
## 2367 United-States <=50K
## 2368 United-States >50K
## 2369 United-States <=50K
## 2370 Mexico <=50K
## 2371 United-States <=50K
## 2372 ? <=50K
## 2373 United-States <=50K
## 2374 Poland <=50K
## 2375 England <=50K
## 2376 United-States <=50K
## 2377 United-States <=50K
## 2378 England >50K
## 2379 United-States <=50K
## 2380 United-States <=50K
## 2381 United-States <=50K
## 2382 United-States <=50K
## 2383 United-States <=50K
## 2384 United-States <=50K
## 2385 United-States <=50K
## 2386 United-States >50K
## 2387 United-States >50K
## 2388 United-States <=50K
## 2389 United-States <=50K
## 2390 United-States <=50K
## 2391 Canada >50K
## 2392 United-States <=50K
## 2393 United-States <=50K
## 2394 United-States <=50K
## 2395 United-States <=50K
## 2396 United-States <=50K
## 2397 United-States >50K
## 2398 United-States <=50K
## 2399 United-States <=50K
## 2400 United-States <=50K
## 2401 United-States <=50K
## 2402 United-States <=50K
## 2403 United-States <=50K
## 2404 United-States >50K
## 2405 United-States <=50K
## 2406 United-States <=50K
## 2407 United-States >50K
## 2408 United-States <=50K
## 2409 United-States <=50K
## 2410 United-States <=50K
## 2411 Mexico <=50K
## 2412 United-States <=50K
## 2413 Philippines <=50K
## 2414 United-States <=50K
## 2415 United-States >50K
## 2416 United-States >50K
## 2417 United-States <=50K
## 2418 United-States <=50K
## 2419 United-States <=50K
## 2420 United-States <=50K
## 2421 United-States >50K
## 2422 United-States <=50K
## 2423 United-States <=50K
## 2424 United-States <=50K
## 2425 United-States <=50K
## 2426 United-States >50K
## 2427 Greece >50K
## 2428 United-States <=50K
## 2429 United-States <=50K
## 2430 United-States <=50K
## 2431 United-States >50K
## 2432 United-States >50K
## 2433 China <=50K
## 2434 United-States >50K
## 2435 United-States <=50K
## 2436 United-States <=50K
## 2437 United-States <=50K
## 2438 United-States <=50K
## 2439 United-States <=50K
## 2440 United-States <=50K
## 2441 United-States <=50K
## 2442 Trinadad&Tobago <=50K
## 2443 United-States <=50K
## 2444 United-States <=50K
## 2445 United-States <=50K
## 2446 United-States <=50K
## 2447 Mexico <=50K
## 2448 United-States <=50K
## 2449 United-States <=50K
## 2450 United-States <=50K
## 2451 United-States <=50K
## 2452 United-States <=50K
## 2453 United-States <=50K
## 2454 United-States <=50K
## 2455 United-States <=50K
## 2456 United-States <=50K
## 2457 United-States <=50K
## 2458 United-States <=50K
## 2459 United-States <=50K
## 2460 United-States <=50K
## 2461 United-States >50K
## 2462 United-States <=50K
## 2463 United-States >50K
## 2464 United-States <=50K
## 2465 United-States <=50K
## 2466 United-States >50K
## 2467 United-States <=50K
## 2468 United-States <=50K
## 2469 United-States <=50K
## 2470 Dominican-Republic >50K
## 2471 United-States >50K
## 2472 United-States <=50K
## 2473 Canada >50K
## 2474 United-States >50K
## 2475 United-States <=50K
## 2476 United-States <=50K
## 2477 United-States <=50K
## 2478 United-States <=50K
## 2479 United-States <=50K
## 2480 United-States <=50K
## 2481 United-States <=50K
## 2482 United-States >50K
## 2483 United-States <=50K
## 2484 United-States <=50K
## 2485 United-States >50K
## 2486 United-States <=50K
## 2487 United-States <=50K
## 2488 United-States <=50K
## 2489 United-States <=50K
## 2490 United-States <=50K
## 2491 United-States <=50K
## 2492 United-States <=50K
## 2493 United-States <=50K
## 2494 United-States <=50K
## 2495 United-States <=50K
## 2496 United-States >50K
## 2497 United-States <=50K
## 2498 United-States >50K
## 2499 United-States >50K
## 2500 United-States <=50K
## 2501 United-States <=50K
## 2502 United-States <=50K
## 2503 Puerto-Rico <=50K
## 2504 United-States <=50K
## 2505 United-States <=50K
## 2506 United-States >50K
## 2507 United-States <=50K
## 2508 United-States <=50K
## 2509 United-States >50K
## 2510 United-States >50K
## 2511 United-States <=50K
## 2512 United-States <=50K
## 2513 ? >50K
## 2514 ? <=50K
## 2515 United-States >50K
## 2516 United-States <=50K
## 2517 United-States <=50K
## 2518 United-States >50K
## 2519 ? <=50K
## 2520 United-States <=50K
## 2521 United-States >50K
## 2522 United-States <=50K
## 2523 United-States <=50K
## 2524 United-States <=50K
## 2525 United-States <=50K
## 2526 United-States <=50K
## 2527 United-States <=50K
## 2528 United-States <=50K
## 2529 United-States <=50K
## 2530 United-States >50K
## 2531 United-States <=50K
## 2532 United-States <=50K
## 2533 United-States <=50K
## 2534 United-States <=50K
## 2535 United-States <=50K
## 2536 United-States <=50K
## 2537 United-States <=50K
## 2538 United-States <=50K
## 2539 United-States >50K
## 2540 United-States <=50K
## 2541 United-States <=50K
## 2542 United-States >50K
## 2543 United-States <=50K
## 2544 United-States <=50K
## 2545 United-States <=50K
## 2546 United-States <=50K
## 2547 United-States >50K
## 2548 United-States <=50K
## 2549 United-States <=50K
## 2550 ? <=50K
## 2551 United-States <=50K
## 2552 United-States <=50K
## 2553 United-States <=50K
## 2554 United-States <=50K
## 2555 United-States <=50K
## 2556 United-States <=50K
## 2557 United-States >50K
## 2558 United-States <=50K
## 2559 India <=50K
## 2560 United-States <=50K
## 2561 United-States >50K
## 2562 Haiti <=50K
## 2563 United-States <=50K
## 2564 United-States <=50K
## 2565 United-States <=50K
## 2566 United-States <=50K
## 2567 United-States <=50K
## 2568 United-States <=50K
## 2569 United-States <=50K
## 2570 United-States >50K
## 2571 United-States >50K
## 2572 United-States <=50K
## 2573 ? <=50K
## 2574 United-States <=50K
## 2575 United-States >50K
## 2576 United-States <=50K
## 2577 United-States >50K
## 2578 United-States <=50K
## 2579 United-States >50K
## 2580 United-States <=50K
## 2581 United-States >50K
## 2582 Dominican-Republic <=50K
## 2583 United-States <=50K
## 2584 United-States >50K
## 2585 Portugal <=50K
## 2586 United-States <=50K
## 2587 Germany <=50K
## 2588 ? <=50K
## 2589 United-States >50K
## 2590 United-States >50K
## 2591 Philippines <=50K
## 2592 ? <=50K
## 2593 United-States <=50K
## 2594 United-States >50K
## 2595 United-States <=50K
## 2596 United-States <=50K
## 2597 United-States <=50K
## 2598 United-States >50K
## 2599 United-States >50K
## 2600 United-States >50K
## 2601 United-States <=50K
## 2602 United-States <=50K
## 2603 United-States <=50K
## 2604 United-States >50K
## 2605 United-States <=50K
## 2606 United-States <=50K
## 2607 United-States <=50K
## 2608 United-States >50K
## 2609 United-States <=50K
## 2610 United-States <=50K
## 2611 United-States <=50K
## 2612 United-States <=50K
## 2613 United-States <=50K
## 2614 United-States <=50K
## 2615 United-States >50K
## 2616 United-States <=50K
## 2617 Mexico <=50K
## 2618 United-States <=50K
## 2619 United-States >50K
## 2620 United-States <=50K
## 2621 United-States >50K
## 2622 United-States <=50K
## 2623 United-States <=50K
## 2624 United-States <=50K
## 2625 United-States >50K
## 2626 United-States <=50K
## 2627 United-States >50K
## 2628 United-States <=50K
## 2629 United-States <=50K
## 2630 United-States <=50K
## 2631 United-States <=50K
## 2632 United-States >50K
## 2633 United-States <=50K
## 2634 United-States >50K
## 2635 United-States <=50K
## 2636 United-States <=50K
## 2637 United-States <=50K
## 2638 United-States >50K
## 2639 United-States >50K
## 2640 ? <=50K
## 2641 United-States <=50K
## 2642 United-States <=50K
## 2643 United-States <=50K
## 2644 United-States >50K
## 2645 United-States <=50K
## 2646 United-States <=50K
## 2647 Nicaragua <=50K
## 2648 Dominican-Republic <=50K
## 2649 United-States <=50K
## 2650 United-States <=50K
## 2651 United-States <=50K
## 2652 United-States <=50K
## 2653 Iran >50K
## 2654 Vietnam <=50K
## 2655 United-States <=50K
## 2656 Hong <=50K
## 2657 United-States <=50K
## 2658 United-States <=50K
## 2659 United-States <=50K
## 2660 Columbia <=50K
## 2661 United-States >50K
## 2662 United-States <=50K
## 2663 United-States <=50K
## 2664 United-States >50K
## 2665 United-States >50K
## 2666 United-States >50K
## 2667 United-States <=50K
## 2668 Mexico <=50K
## 2669 Cuba <=50K
## 2670 United-States >50K
## 2671 United-States >50K
## 2672 United-States <=50K
## 2673 United-States <=50K
## 2674 United-States <=50K
## 2675 Mexico <=50K
## 2676 United-States <=50K
## 2677 United-States <=50K
## 2678 United-States <=50K
## 2679 United-States <=50K
## 2680 United-States <=50K
## 2681 El-Salvador <=50K
## 2682 United-States <=50K
## 2683 United-States <=50K
## 2684 United-States >50K
## 2685 United-States <=50K
## 2686 United-States <=50K
## 2687 United-States <=50K
## 2688 United-States >50K
## 2689 United-States <=50K
## 2690 United-States <=50K
## 2691 United-States <=50K
## 2692 United-States <=50K
## 2693 United-States <=50K
## 2694 United-States <=50K
## 2695 United-States <=50K
## 2696 United-States >50K
## 2697 United-States <=50K
## 2698 United-States <=50K
## 2699 United-States <=50K
## 2700 United-States <=50K
## 2701 United-States <=50K
## 2702 United-States <=50K
## 2703 United-States <=50K
## 2704 United-States <=50K
## 2705 United-States <=50K
## 2706 United-States <=50K
## 2707 United-States <=50K
## 2708 United-States >50K
## 2709 United-States <=50K
## 2710 United-States <=50K
## 2711 United-States >50K
## 2712 United-States <=50K
## 2713 United-States <=50K
## 2714 United-States >50K
## 2715 United-States <=50K
## 2716 United-States <=50K
## 2717 United-States >50K
## 2718 ? <=50K
## 2719 United-States <=50K
## 2720 United-States >50K
## 2721 United-States <=50K
## 2722 United-States >50K
## 2723 Mexico <=50K
## 2724 United-States <=50K
## 2725 United-States >50K
## 2726 United-States >50K
## 2727 United-States >50K
## 2728 United-States <=50K
## 2729 United-States <=50K
## 2730 United-States <=50K
## 2731 United-States <=50K
## 2732 United-States >50K
## 2733 United-States <=50K
## 2734 United-States <=50K
## 2735 United-States >50K
## 2736 ? <=50K
## 2737 United-States >50K
## 2738 United-States <=50K
## 2739 United-States >50K
## 2740 United-States <=50K
## 2741 United-States >50K
## 2742 United-States <=50K
## 2743 United-States <=50K
## 2744 United-States <=50K
## 2745 United-States >50K
## 2746 United-States <=50K
## 2747 United-States <=50K
## 2748 United-States <=50K
## 2749 United-States <=50K
## 2750 Nicaragua <=50K
## 2751 United-States <=50K
## 2752 United-States <=50K
## 2753 Greece >50K
## 2754 United-States >50K
## 2755 United-States <=50K
## 2756 Mexico <=50K
## 2757 United-States >50K
## 2758 United-States <=50K
## 2759 United-States <=50K
## 2760 United-States >50K
## 2761 United-States <=50K
## 2762 United-States <=50K
## 2763 United-States >50K
## 2764 United-States <=50K
## 2765 United-States <=50K
## 2766 United-States <=50K
## 2767 United-States <=50K
## 2768 Canada >50K
## 2769 United-States <=50K
## 2770 United-States <=50K
## 2771 United-States <=50K
## 2772 United-States <=50K
## 2773 United-States <=50K
## 2774 United-States <=50K
## 2775 United-States >50K
## 2776 ? >50K
## 2777 United-States <=50K
## 2778 United-States <=50K
## 2779 United-States <=50K
## 2780 Greece <=50K
## 2781 United-States <=50K
## 2782 United-States >50K
## 2783 United-States <=50K
## 2784 United-States >50K
## 2785 United-States <=50K
## 2786 United-States <=50K
## 2787 United-States <=50K
## 2788 United-States <=50K
## 2789 Germany <=50K
## 2790 United-States <=50K
## 2791 Cuba <=50K
## 2792 United-States <=50K
## 2793 United-States <=50K
## 2794 United-States <=50K
## 2795 ? <=50K
## 2796 United-States <=50K
## 2797 United-States >50K
## 2798 United-States <=50K
## 2799 United-States <=50K
## 2800 United-States <=50K
## 2801 United-States >50K
## 2802 United-States <=50K
## 2803 United-States <=50K
## 2804 United-States <=50K
## 2805 United-States <=50K
## 2806 United-States <=50K
## 2807 United-States <=50K
## 2808 United-States <=50K
## 2809 United-States <=50K
## 2810 United-States <=50K
## 2811 United-States <=50K
## 2812 United-States <=50K
## 2813 United-States <=50K
## 2814 United-States <=50K
## 2815 United-States <=50K
## 2816 Mexico <=50K
## 2817 United-States <=50K
## 2818 Mexico >50K
## 2819 United-States <=50K
## 2820 United-States <=50K
## 2821 United-States <=50K
## 2822 United-States <=50K
## 2823 United-States <=50K
## 2824 United-States <=50K
## 2825 United-States <=50K
## 2826 United-States <=50K
## 2827 United-States <=50K
## 2828 United-States <=50K
## 2829 United-States >50K
## 2830 United-States >50K
## 2831 United-States <=50K
## 2832 United-States <=50K
## 2833 United-States >50K
## 2834 United-States >50K
## 2835 Japan <=50K
## 2836 United-States <=50K
## 2837 United-States <=50K
## 2838 United-States <=50K
## 2839 United-States <=50K
## 2840 United-States <=50K
## 2841 United-States >50K
## 2842 United-States <=50K
## 2843 United-States <=50K
## 2844 United-States >50K
## 2845 United-States >50K
## 2846 United-States >50K
## 2847 United-States <=50K
## 2848 United-States <=50K
## 2849 United-States >50K
## 2850 United-States <=50K
## 2851 United-States <=50K
## 2852 United-States <=50K
## 2853 United-States <=50K
## 2854 United-States <=50K
## 2855 United-States <=50K
## 2856 United-States <=50K
## 2857 United-States <=50K
## 2858 Canada <=50K
## 2859 United-States <=50K
## 2860 United-States >50K
## 2861 United-States >50K
## 2862 El-Salvador <=50K
## 2863 United-States <=50K
## 2864 United-States <=50K
## 2865 United-States <=50K
## 2866 United-States <=50K
## 2867 United-States <=50K
## 2868 United-States <=50K
## 2869 United-States <=50K
## 2870 United-States <=50K
## 2871 United-States <=50K
## 2872 United-States >50K
## 2873 United-States <=50K
## 2874 United-States <=50K
## 2875 United-States >50K
## 2876 United-States <=50K
## 2877 Mexico <=50K
## 2878 United-States <=50K
## 2879 United-States <=50K
## 2880 United-States <=50K
## 2881 United-States <=50K
## 2882 United-States <=50K
## 2883 United-States <=50K
## 2884 United-States >50K
## 2885 United-States <=50K
## 2886 United-States <=50K
## 2887 United-States >50K
## 2888 United-States <=50K
## 2889 United-States <=50K
## 2890 United-States <=50K
## 2891 United-States <=50K
## 2892 Puerto-Rico <=50K
## 2893 United-States <=50K
## 2894 United-States <=50K
## 2895 United-States >50K
## 2896 United-States <=50K
## 2897 United-States >50K
## 2898 United-States <=50K
## 2899 Taiwan <=50K
## 2900 Germany <=50K
## 2901 Italy >50K
## 2902 United-States >50K
## 2903 United-States <=50K
## 2904 United-States <=50K
## 2905 United-States <=50K
## 2906 United-States <=50K
## 2907 United-States <=50K
## 2908 United-States <=50K
## 2909 United-States <=50K
## 2910 ? >50K
## 2911 United-States <=50K
## 2912 United-States <=50K
## 2913 United-States <=50K
## 2914 United-States <=50K
## 2915 United-States <=50K
## 2916 United-States <=50K
## 2917 United-States <=50K
## 2918 United-States >50K
## 2919 United-States <=50K
## 2920 United-States >50K
## 2921 United-States <=50K
## 2922 United-States <=50K
## 2923 United-States <=50K
## 2924 United-States <=50K
## 2925 Canada <=50K
## 2926 United-States >50K
## 2927 ? <=50K
## 2928 United-States <=50K
## 2929 United-States <=50K
## 2930 United-States <=50K
## 2931 United-States <=50K
## 2932 United-States <=50K
## 2933 United-States <=50K
## 2934 Italy >50K
## 2935 United-States <=50K
## 2936 United-States <=50K
## 2937 United-States <=50K
## 2938 United-States <=50K
## 2939 United-States <=50K
## 2940 United-States <=50K
## 2941 United-States <=50K
## 2942 United-States >50K
## 2943 United-States <=50K
## 2944 United-States <=50K
## 2945 United-States <=50K
## 2946 United-States <=50K
## 2947 Mexico <=50K
## 2948 United-States <=50K
## 2949 United-States >50K
## 2950 United-States <=50K
## 2951 United-States >50K
## 2952 United-States <=50K
## 2953 United-States <=50K
## 2954 United-States <=50K
## 2955 United-States <=50K
## 2956 United-States <=50K
## 2957 United-States >50K
## 2958 United-States <=50K
## 2959 United-States <=50K
## 2960 United-States <=50K
## 2961 United-States <=50K
## 2962 United-States <=50K
## 2963 United-States >50K
## 2964 United-States <=50K
## 2965 United-States <=50K
## 2966 United-States >50K
## 2967 Ireland <=50K
## 2968 United-States <=50K
## 2969 United-States >50K
## 2970 United-States <=50K
## 2971 Mexico <=50K
## 2972 United-States >50K
## 2973 United-States <=50K
## 2974 United-States >50K
## 2975 United-States <=50K
## 2976 United-States <=50K
## 2977 United-States <=50K
## 2978 United-States >50K
## 2979 United-States <=50K
## 2980 United-States <=50K
## 2981 United-States >50K
## 2982 United-States <=50K
## 2983 United-States <=50K
## 2984 United-States <=50K
## 2985 United-States <=50K
## 2986 United-States <=50K
## 2987 United-States >50K
## 2988 United-States <=50K
## 2989 United-States <=50K
## 2990 India <=50K
## 2991 United-States <=50K
## 2992 United-States <=50K
## 2993 Columbia <=50K
## 2994 United-States <=50K
## 2995 Japan <=50K
## 2996 United-States <=50K
## 2997 United-States <=50K
## 2998 United-States <=50K
## 2999 Mexico <=50K
## 3000 United-States <=50K
## 3001 United-States <=50K
## 3002 United-States <=50K
## 3003 United-States <=50K
## 3004 United-States <=50K
## 3005 United-States <=50K
## 3006 United-States <=50K
## 3007 United-States <=50K
## 3008 United-States <=50K
## 3009 United-States <=50K
## 3010 United-States <=50K
## 3011 United-States <=50K
## 3012 United-States >50K
## 3013 United-States >50K
## 3014 United-States >50K
## 3015 United-States >50K
## 3016 United-States <=50K
## 3017 United-States <=50K
## 3018 United-States >50K
## 3019 United-States <=50K
## 3020 United-States <=50K
## 3021 United-States >50K
## 3022 Philippines >50K
## 3023 United-States >50K
## 3024 ? <=50K
## 3025 United-States <=50K
## 3026 England <=50K
## 3027 United-States <=50K
## 3028 United-States >50K
## 3029 United-States <=50K
## 3030 United-States <=50K
## 3031 United-States <=50K
## 3032 United-States <=50K
## 3033 United-States >50K
## 3034 United-States <=50K
## 3035 United-States <=50K
## 3036 United-States >50K
## 3037 United-States <=50K
## 3038 United-States <=50K
## 3039 United-States <=50K
## 3040 United-States >50K
## 3041 United-States >50K
## 3042 United-States <=50K
## 3043 United-States <=50K
## 3044 Mexico <=50K
## 3045 United-States <=50K
## 3046 United-States <=50K
## 3047 United-States <=50K
## 3048 United-States <=50K
## 3049 United-States <=50K
## 3050 United-States >50K
## 3051 United-States >50K
## 3052 United-States <=50K
## 3053 United-States >50K
## 3054 United-States <=50K
## 3055 United-States <=50K
## 3056 United-States <=50K
## 3057 United-States >50K
## 3058 United-States <=50K
## 3059 United-States <=50K
## 3060 United-States <=50K
## 3061 United-States <=50K
## 3062 United-States <=50K
## 3063 United-States <=50K
## 3064 United-States <=50K
## 3065 Japan >50K
## 3066 United-States <=50K
## 3067 United-States <=50K
## 3068 United-States <=50K
## 3069 United-States <=50K
## 3070 United-States <=50K
## 3071 United-States <=50K
## 3072 United-States >50K
## 3073 United-States <=50K
## 3074 United-States <=50K
## 3075 United-States <=50K
## 3076 United-States <=50K
## 3077 United-States <=50K
## 3078 United-States >50K
## 3079 United-States <=50K
## 3080 United-States >50K
## 3081 United-States >50K
## 3082 United-States <=50K
## 3083 United-States <=50K
## 3084 United-States <=50K
## 3085 United-States <=50K
## 3086 United-States <=50K
## 3087 United-States <=50K
## 3088 United-States <=50K
## 3089 United-States <=50K
## 3090 United-States >50K
## 3091 United-States <=50K
## 3092 United-States >50K
## 3093 United-States <=50K
## 3094 United-States >50K
## 3095 United-States <=50K
## 3096 United-States >50K
## 3097 United-States <=50K
## 3098 United-States <=50K
## 3099 United-States <=50K
## 3100 United-States <=50K
## 3101 United-States <=50K
## 3102 United-States <=50K
## 3103 United-States <=50K
## 3104 United-States <=50K
## 3105 United-States <=50K
## 3106 United-States >50K
## 3107 United-States >50K
## 3108 ? >50K
## 3109 United-States >50K
## 3110 United-States <=50K
## 3111 United-States <=50K
## 3112 United-States >50K
## 3113 United-States <=50K
## 3114 United-States >50K
## 3115 United-States <=50K
## 3116 Poland <=50K
## 3117 United-States <=50K
## 3118 United-States <=50K
## 3119 Mexico <=50K
## 3120 United-States <=50K
## 3121 United-States <=50K
## 3122 United-States <=50K
## 3123 United-States >50K
## 3124 United-States <=50K
## 3125 United-States <=50K
## 3126 United-States >50K
## 3127 United-States <=50K
## 3128 United-States <=50K
## 3129 United-States <=50K
## 3130 United-States >50K
## 3131 United-States <=50K
## 3132 ? <=50K
## 3133 United-States <=50K
## 3134 United-States >50K
## 3135 United-States >50K
## 3136 United-States <=50K
## 3137 United-States <=50K
## 3138 United-States >50K
## 3139 United-States <=50K
## 3140 United-States <=50K
## 3141 United-States >50K
## 3142 United-States <=50K
## 3143 United-States <=50K
## 3144 United-States >50K
## 3145 United-States >50K
## 3146 United-States <=50K
## 3147 United-States <=50K
## 3148 United-States <=50K
## 3149 United-States <=50K
## 3150 United-States <=50K
## 3151 United-States <=50K
## 3152 United-States <=50K
## 3153 United-States >50K
## 3154 United-States >50K
## 3155 United-States >50K
## 3156 United-States >50K
## 3157 United-States >50K
## 3158 United-States <=50K
## 3159 United-States <=50K
## 3160 Germany >50K
## 3161 United-States <=50K
## 3162 United-States <=50K
## 3163 United-States <=50K
## 3164 United-States >50K
## 3165 ? >50K
## 3166 Mexico <=50K
## 3167 ? >50K
## 3168 United-States >50K
## 3169 United-States >50K
## 3170 United-States <=50K
## 3171 United-States <=50K
## 3172 United-States <=50K
## 3173 United-States <=50K
## 3174 United-States <=50K
## 3175 United-States <=50K
## 3176 United-States >50K
## 3177 United-States >50K
## 3178 United-States <=50K
## 3179 United-States <=50K
## 3180 Trinadad&Tobago <=50K
## 3181 United-States >50K
## 3182 United-States <=50K
## 3183 United-States <=50K
## 3184 United-States <=50K
## 3185 United-States >50K
## 3186 United-States >50K
## 3187 United-States <=50K
## 3188 ? <=50K
## 3189 United-States <=50K
## 3190 United-States <=50K
## 3191 United-States >50K
## 3192 United-States <=50K
## 3193 United-States <=50K
## 3194 United-States <=50K
## 3195 United-States <=50K
## 3196 United-States <=50K
## 3197 Mexico <=50K
## 3198 United-States <=50K
## 3199 United-States >50K
## 3200 United-States >50K
## 3201 ? <=50K
## 3202 Dominican-Republic <=50K
## 3203 South <=50K
## 3204 United-States <=50K
## 3205 United-States >50K
## 3206 United-States <=50K
## 3207 United-States >50K
## 3208 United-States <=50K
## 3209 United-States <=50K
## 3210 United-States <=50K
## 3211 United-States <=50K
## 3212 United-States <=50K
## 3213 United-States >50K
## 3214 United-States <=50K
## 3215 United-States <=50K
## 3216 Greece <=50K
## 3217 United-States >50K
## 3218 United-States >50K
## 3219 United-States >50K
## 3220 United-States <=50K
## 3221 United-States >50K
## 3222 United-States <=50K
## 3223 United-States <=50K
## 3224 United-States <=50K
## 3225 United-States >50K
## 3226 United-States >50K
## 3227 United-States <=50K
## 3228 United-States <=50K
## 3229 United-States <=50K
## 3230 United-States <=50K
## 3231 United-States >50K
## 3232 United-States <=50K
## 3233 ? <=50K
## 3234 United-States <=50K
## 3235 United-States <=50K
## 3236 United-States <=50K
## 3237 Poland <=50K
## 3238 Mexico <=50K
## 3239 Puerto-Rico <=50K
## 3240 Philippines <=50K
## 3241 United-States <=50K
## 3242 United-States <=50K
## 3243 United-States <=50K
## 3244 Mexico <=50K
## 3245 United-States <=50K
## 3246 United-States >50K
## 3247 United-States <=50K
## 3248 ? >50K
## 3249 United-States <=50K
## 3250 United-States >50K
## 3251 United-States <=50K
## 3252 United-States <=50K
## 3253 United-States >50K
## 3254 United-States <=50K
## 3255 United-States >50K
## 3256 United-States <=50K
## 3257 ? >50K
## 3258 United-States <=50K
## 3259 United-States <=50K
## 3260 United-States <=50K
## 3261 Peru >50K
## 3262 United-States <=50K
## 3263 United-States >50K
## 3264 United-States <=50K
## 3265 United-States <=50K
## 3266 United-States >50K
## 3267 United-States <=50K
## 3268 United-States >50K
## 3269 United-States >50K
## 3270 United-States <=50K
## 3271 United-States <=50K
## 3272 United-States >50K
## 3273 United-States <=50K
## 3274 United-States >50K
## 3275 United-States >50K
## 3276 United-States <=50K
## 3277 United-States <=50K
## 3278 United-States <=50K
## 3279 United-States >50K
## 3280 United-States <=50K
## 3281 United-States <=50K
## 3282 Mexico >50K
## 3283 United-States <=50K
## 3284 United-States <=50K
## 3285 United-States <=50K
## 3286 United-States <=50K
## 3287 Mexico <=50K
## 3288 United-States <=50K
## 3289 United-States <=50K
## 3290 Cuba <=50K
## 3291 United-States >50K
## 3292 United-States <=50K
## 3293 United-States >50K
## 3294 United-States <=50K
## 3295 United-States <=50K
## 3296 United-States <=50K
## 3297 United-States <=50K
## 3298 Canada >50K
## 3299 United-States <=50K
## 3300 United-States <=50K
## 3301 United-States <=50K
## 3302 United-States <=50K
## 3303 United-States <=50K
## 3304 El-Salvador <=50K
## 3305 United-States <=50K
## 3306 United-States <=50K
## 3307 United-States <=50K
## 3308 United-States <=50K
## 3309 United-States <=50K
## 3310 United-States <=50K
## 3311 United-States <=50K
## 3312 United-States <=50K
## 3313 United-States <=50K
## 3314 United-States <=50K
## 3315 United-States <=50K
## 3316 United-States <=50K
## 3317 United-States <=50K
## 3318 Guatemala >50K
## 3319 United-States <=50K
## 3320 United-States <=50K
## 3321 United-States <=50K
## 3322 United-States >50K
## 3323 United-States <=50K
## 3324 United-States <=50K
## 3325 United-States <=50K
## 3326 United-States <=50K
## 3327 United-States <=50K
## 3328 United-States <=50K
## 3329 United-States >50K
## 3330 United-States <=50K
## 3331 United-States <=50K
## 3332 United-States <=50K
## 3333 United-States <=50K
## 3334 United-States >50K
## 3335 United-States <=50K
## 3336 United-States <=50K
## 3337 United-States >50K
## 3338 United-States >50K
## 3339 United-States <=50K
## 3340 United-States <=50K
## 3341 United-States <=50K
## 3342 United-States <=50K
## 3343 United-States <=50K
## 3344 Mexico <=50K
## 3345 United-States <=50K
## 3346 United-States <=50K
## 3347 United-States <=50K
## 3348 Mexico <=50K
## 3349 Germany <=50K
## 3350 United-States >50K
## 3351 Mexico <=50K
## 3352 United-States <=50K
## 3353 United-States <=50K
## 3354 United-States >50K
## 3355 United-States <=50K
## 3356 United-States <=50K
## 3357 United-States <=50K
## 3358 Dominican-Republic <=50K
## 3359 United-States >50K
## 3360 United-States >50K
## 3361 United-States >50K
## 3362 Mexico <=50K
## 3363 United-States <=50K
## 3364 United-States <=50K
## 3365 United-States <=50K
## 3366 United-States >50K
## 3367 United-States <=50K
## 3368 United-States <=50K
## 3369 United-States >50K
## 3370 United-States <=50K
## 3371 United-States <=50K
## 3372 United-States <=50K
## 3373 United-States <=50K
## 3374 United-States <=50K
## 3375 United-States <=50K
## 3376 United-States <=50K
## 3377 United-States <=50K
## 3378 United-States <=50K
## 3379 United-States <=50K
## 3380 United-States <=50K
## 3381 Mexico <=50K
## 3382 United-States >50K
## 3383 United-States >50K
## 3384 United-States <=50K
## 3385 United-States >50K
## 3386 United-States <=50K
## 3387 Columbia <=50K
## 3388 United-States >50K
## 3389 United-States >50K
## 3390 Iran >50K
## 3391 Iran >50K
## 3392 United-States <=50K
## 3393 United-States <=50K
## 3394 United-States <=50K
## 3395 United-States <=50K
## 3396 United-States >50K
## 3397 United-States <=50K
## 3398 United-States <=50K
## 3399 United-States >50K
## 3400 United-States <=50K
## 3401 United-States <=50K
## 3402 United-States >50K
## 3403 United-States <=50K
## 3404 United-States <=50K
## 3405 Mexico <=50K
## 3406 United-States <=50K
## 3407 United-States <=50K
## 3408 United-States >50K
## 3409 United-States >50K
## 3410 United-States <=50K
## 3411 United-States >50K
## 3412 United-States <=50K
## 3413 United-States <=50K
## 3414 United-States >50K
## 3415 United-States <=50K
## 3416 United-States <=50K
## 3417 United-States <=50K
## 3418 United-States <=50K
## 3419 United-States >50K
## 3420 United-States <=50K
## 3421 United-States <=50K
## 3422 United-States <=50K
## 3423 United-States <=50K
## 3424 United-States <=50K
## 3425 Mexico <=50K
## 3426 United-States <=50K
## 3427 United-States <=50K
## 3428 United-States <=50K
## 3429 United-States <=50K
## 3430 United-States <=50K
## 3431 United-States >50K
## 3432 United-States <=50K
## 3433 United-States <=50K
## 3434 United-States <=50K
## 3435 United-States <=50K
## 3436 United-States >50K
## 3437 United-States <=50K
## 3438 United-States <=50K
## 3439 United-States >50K
## 3440 United-States <=50K
## 3441 United-States <=50K
## 3442 United-States <=50K
## 3443 United-States >50K
## 3444 Mexico <=50K
## 3445 United-States <=50K
## 3446 United-States >50K
## 3447 Mexico <=50K
## 3448 Italy <=50K
## 3449 El-Salvador >50K
## 3450 United-States <=50K
## 3451 United-States <=50K
## 3452 United-States >50K
## 3453 United-States <=50K
## 3454 United-States <=50K
## 3455 United-States <=50K
## 3456 United-States <=50K
## 3457 United-States <=50K
## 3458 United-States <=50K
## 3459 United-States <=50K
## 3460 United-States <=50K
## 3461 United-States >50K
## 3462 ? <=50K
## 3463 United-States <=50K
## 3464 United-States <=50K
## 3465 United-States >50K
## 3466 United-States <=50K
## 3467 United-States >50K
## 3468 United-States <=50K
## 3469 United-States >50K
## 3470 United-States <=50K
## 3471 United-States <=50K
## 3472 United-States <=50K
## 3473 United-States <=50K
## 3474 United-States <=50K
## 3475 United-States <=50K
## 3476 United-States >50K
## 3477 United-States <=50K
## 3478 United-States >50K
## 3479 United-States >50K
## 3480 United-States <=50K
## 3481 United-States >50K
## 3482 Portugal <=50K
## 3483 United-States <=50K
## 3484 England <=50K
## 3485 ? <=50K
## 3486 United-States >50K
## 3487 United-States <=50K
## 3488 United-States <=50K
## 3489 United-States <=50K
## 3490 United-States >50K
## 3491 United-States <=50K
## 3492 United-States <=50K
## 3493 United-States <=50K
## 3494 United-States >50K
## 3495 United-States >50K
## 3496 ? <=50K
## 3497 United-States <=50K
## 3498 United-States >50K
## 3499 United-States <=50K
## 3500 United-States <=50K
## 3501 United-States <=50K
## 3502 United-States <=50K
## 3503 Mexico >50K
## 3504 United-States <=50K
## 3505 United-States <=50K
## 3506 United-States <=50K
## 3507 United-States <=50K
## 3508 United-States <=50K
## 3509 United-States <=50K
## 3510 United-States <=50K
## 3511 United-States <=50K
## 3512 Cuba <=50K
## 3513 United-States <=50K
## 3514 United-States >50K
## 3515 United-States <=50K
## 3516 United-States >50K
## 3517 United-States <=50K
## 3518 United-States >50K
## 3519 United-States >50K
## 3520 United-States <=50K
## 3521 United-States <=50K
## 3522 United-States <=50K
## 3523 United-States <=50K
## 3524 United-States >50K
## 3525 United-States <=50K
## 3526 United-States <=50K
## 3527 United-States <=50K
## 3528 United-States <=50K
## 3529 United-States <=50K
## 3530 United-States <=50K
## 3531 United-States >50K
## 3532 United-States <=50K
## 3533 ? <=50K
## 3534 Cuba <=50K
## 3535 United-States <=50K
## 3536 United-States <=50K
## 3537 United-States >50K
## 3538 United-States <=50K
## 3539 United-States >50K
## 3540 United-States >50K
## 3541 United-States >50K
## 3542 United-States <=50K
## 3543 United-States <=50K
## 3544 United-States >50K
## 3545 United-States <=50K
## 3546 United-States <=50K
## 3547 United-States <=50K
## 3548 United-States <=50K
## 3549 United-States <=50K
## 3550 United-States <=50K
## 3551 United-States <=50K
## 3552 Jamaica <=50K
## 3553 United-States >50K
## 3554 United-States <=50K
## 3555 United-States <=50K
## 3556 United-States <=50K
## 3557 United-States <=50K
## 3558 United-States <=50K
## 3559 United-States <=50K
## 3560 United-States >50K
## 3561 United-States <=50K
## 3562 United-States >50K
## 3563 United-States <=50K
## 3564 United-States <=50K
## 3565 United-States <=50K
## 3566 United-States <=50K
## 3567 United-States <=50K
## 3568 United-States <=50K
## 3569 United-States >50K
## 3570 United-States <=50K
## 3571 Mexico <=50K
## 3572 United-States <=50K
## 3573 United-States <=50K
## 3574 Mexico <=50K
## 3575 United-States >50K
## 3576 United-States >50K
## 3577 United-States <=50K
## 3578 United-States >50K
## 3579 United-States >50K
## 3580 ? <=50K
## 3581 United-States >50K
## 3582 United-States <=50K
## 3583 United-States <=50K
## 3584 United-States >50K
## 3585 United-States >50K
## 3586 United-States <=50K
## 3587 United-States <=50K
## 3588 United-States <=50K
## 3589 United-States <=50K
## 3590 United-States <=50K
## 3591 United-States <=50K
## 3592 United-States <=50K
## 3593 El-Salvador <=50K
## 3594 United-States <=50K
## 3595 United-States >50K
## 3596 United-States >50K
## 3597 United-States <=50K
## 3598 United-States <=50K
## 3599 United-States <=50K
## 3600 United-States >50K
## 3601 United-States <=50K
## 3602 United-States <=50K
## 3603 United-States >50K
## 3604 United-States <=50K
## 3605 United-States <=50K
## 3606 United-States <=50K
## 3607 Canada <=50K
## 3608 United-States <=50K
## 3609 Jamaica <=50K
## 3610 Canada <=50K
## 3611 United-States <=50K
## 3612 United-States <=50K
## 3613 United-States >50K
## 3614 United-States <=50K
## 3615 United-States <=50K
## 3616 United-States <=50K
## 3617 United-States <=50K
## 3618 United-States <=50K
## 3619 United-States <=50K
## 3620 United-States <=50K
## 3621 United-States <=50K
## 3622 United-States <=50K
## 3623 United-States <=50K
## 3624 Mexico <=50K
## 3625 United-States <=50K
## 3626 United-States <=50K
## 3627 United-States <=50K
## 3628 China <=50K
## 3629 United-States <=50K
## 3630 United-States <=50K
## 3631 United-States <=50K
## 3632 United-States <=50K
## 3633 United-States <=50K
## 3634 United-States <=50K
## 3635 United-States <=50K
## 3636 United-States <=50K
## 3637 ? <=50K
## 3638 United-States <=50K
## 3639 United-States <=50K
## 3640 United-States >50K
## 3641 United-States <=50K
## 3642 United-States <=50K
## 3643 United-States <=50K
## 3644 United-States <=50K
## 3645 United-States <=50K
## 3646 United-States <=50K
## 3647 United-States <=50K
## 3648 China <=50K
## 3649 United-States <=50K
## 3650 United-States <=50K
## 3651 United-States <=50K
## 3652 United-States >50K
## 3653 United-States >50K
## 3654 United-States <=50K
## 3655 United-States >50K
## 3656 United-States >50K
## 3657 Mexico <=50K
## 3658 United-States >50K
## 3659 United-States <=50K
## 3660 United-States >50K
## 3661 United-States <=50K
## 3662 United-States <=50K
## 3663 United-States <=50K
## 3664 United-States >50K
## 3665 United-States >50K
## 3666 United-States >50K
## 3667 France >50K
## 3668 United-States <=50K
## 3669 Mexico <=50K
## 3670 United-States <=50K
## 3671 United-States <=50K
## 3672 United-States <=50K
## 3673 United-States <=50K
## 3674 Germany >50K
## 3675 United-States >50K
## 3676 Guatemala <=50K
## 3677 United-States <=50K
## 3678 United-States >50K
## 3679 United-States <=50K
## 3680 United-States <=50K
## 3681 England <=50K
## 3682 United-States <=50K
## 3683 United-States <=50K
## 3684 United-States <=50K
## 3685 United-States <=50K
## 3686 United-States <=50K
## 3687 United-States <=50K
## 3688 Mexico <=50K
## 3689 United-States <=50K
## 3690 United-States <=50K
## 3691 United-States <=50K
## 3692 United-States <=50K
## 3693 United-States >50K
## 3694 United-States <=50K
## 3695 United-States <=50K
## 3696 United-States <=50K
## 3697 Mexico <=50K
## 3698 United-States <=50K
## 3699 United-States >50K
## 3700 United-States <=50K
## 3701 United-States >50K
## 3702 United-States <=50K
## 3703 United-States <=50K
## 3704 United-States <=50K
## 3705 United-States <=50K
## 3706 United-States <=50K
## 3707 United-States <=50K
## 3708 United-States >50K
## 3709 Vietnam <=50K
## 3710 United-States <=50K
## 3711 United-States <=50K
## 3712 United-States <=50K
## 3713 United-States <=50K
## 3714 United-States <=50K
## 3715 China >50K
## 3716 United-States <=50K
## 3717 United-States >50K
## 3718 United-States <=50K
## 3719 United-States <=50K
## 3720 Guatemala <=50K
## 3721 United-States <=50K
## 3722 United-States >50K
## 3723 United-States >50K
## 3724 United-States >50K
## 3725 United-States >50K
## 3726 United-States <=50K
## 3727 United-States >50K
## 3728 United-States >50K
## 3729 United-States <=50K
## 3730 United-States <=50K
## 3731 United-States <=50K
## 3732 United-States <=50K
## 3733 United-States >50K
## 3734 United-States >50K
## 3735 United-States <=50K
## 3736 United-States <=50K
## 3737 United-States <=50K
## 3738 United-States <=50K
## 3739 United-States <=50K
## 3740 United-States <=50K
## 3741 United-States >50K
## 3742 United-States <=50K
## 3743 United-States <=50K
## 3744 Germany <=50K
## 3745 Mexico <=50K
## 3746 United-States <=50K
## 3747 United-States <=50K
## 3748 United-States <=50K
## 3749 United-States <=50K
## 3750 United-States >50K
## 3751 United-States <=50K
## 3752 United-States <=50K
## 3753 United-States <=50K
## 3754 United-States <=50K
## 3755 United-States >50K
## 3756 United-States <=50K
## 3757 United-States <=50K
## 3758 United-States <=50K
## 3759 United-States >50K
## 3760 United-States <=50K
## 3761 United-States >50K
## 3762 United-States <=50K
## 3763 United-States <=50K
## 3764 United-States >50K
## 3765 United-States <=50K
## 3766 United-States <=50K
## 3767 United-States <=50K
## 3768 United-States <=50K
## 3769 United-States <=50K
## 3770 United-States <=50K
## 3771 United-States <=50K
## 3772 United-States <=50K
## 3773 United-States <=50K
## 3774 United-States <=50K
## 3775 United-States >50K
## 3776 United-States <=50K
## 3777 United-States <=50K
## 3778 United-States <=50K
## 3779 United-States <=50K
## 3780 United-States >50K
## 3781 United-States <=50K
## 3782 United-States <=50K
## 3783 United-States <=50K
## 3784 United-States <=50K
## 3785 United-States <=50K
## 3786 United-States <=50K
## 3787 United-States >50K
## 3788 United-States <=50K
## 3789 United-States >50K
## 3790 United-States >50K
## 3791 United-States <=50K
## 3792 United-States <=50K
## 3793 United-States <=50K
## 3794 United-States <=50K
## 3795 United-States <=50K
## 3796 United-States >50K
## 3797 United-States <=50K
## 3798 United-States <=50K
## 3799 Guatemala <=50K
## 3800 United-States <=50K
## 3801 United-States <=50K
## 3802 United-States <=50K
## 3803 United-States <=50K
## 3804 United-States <=50K
## 3805 United-States <=50K
## 3806 United-States <=50K
## 3807 United-States >50K
## 3808 United-States <=50K
## 3809 United-States <=50K
## 3810 United-States >50K
## 3811 United-States <=50K
## 3812 United-States <=50K
## 3813 United-States >50K
## 3814 United-States <=50K
## 3815 United-States <=50K
## 3816 United-States <=50K
## 3817 United-States <=50K
## 3818 United-States >50K
## 3819 United-States <=50K
## 3820 United-States <=50K
## 3821 United-States <=50K
## 3822 Mexico <=50K
## 3823 United-States <=50K
## 3824 United-States <=50K
## 3825 United-States <=50K
## 3826 United-States <=50K
## 3827 United-States <=50K
## 3828 United-States <=50K
## 3829 United-States <=50K
## 3830 United-States >50K
## 3831 United-States <=50K
## 3832 United-States >50K
## 3833 Vietnam <=50K
## 3834 United-States <=50K
## 3835 ? <=50K
## 3836 United-States <=50K
## 3837 United-States >50K
## 3838 United-States <=50K
## 3839 United-States <=50K
## 3840 United-States <=50K
## 3841 United-States <=50K
## 3842 United-States <=50K
## 3843 United-States <=50K
## 3844 United-States <=50K
## 3845 United-States <=50K
## 3846 United-States >50K
## 3847 United-States <=50K
## 3848 United-States >50K
## 3849 United-States >50K
## 3850 United-States <=50K
## 3851 United-States >50K
## 3852 United-States <=50K
## 3853 United-States <=50K
## 3854 United-States <=50K
## 3855 United-States <=50K
## 3856 United-States <=50K
## 3857 ? <=50K
## 3858 United-States <=50K
## 3859 ? <=50K
## 3860 United-States <=50K
## 3861 United-States >50K
## 3862 United-States <=50K
## 3863 United-States >50K
## 3864 United-States <=50K
## 3865 United-States >50K
## 3866 United-States >50K
## 3867 United-States >50K
## 3868 United-States >50K
## 3869 United-States <=50K
## 3870 United-States <=50K
## 3871 United-States <=50K
## 3872 United-States <=50K
## 3873 United-States <=50K
## 3874 United-States <=50K
## 3875 United-States <=50K
## 3876 United-States <=50K
## 3877 United-States <=50K
## 3878 United-States <=50K
## 3879 United-States <=50K
## 3880 Vietnam >50K
## 3881 United-States >50K
## 3882 United-States <=50K
## 3883 United-States >50K
## 3884 United-States <=50K
## 3885 United-States >50K
## 3886 United-States <=50K
## 3887 United-States >50K
## 3888 United-States >50K
## 3889 United-States <=50K
## 3890 United-States >50K
## 3891 United-States <=50K
## 3892 United-States <=50K
## 3893 United-States >50K
## 3894 United-States <=50K
## 3895 United-States >50K
## 3896 United-States >50K
## 3897 United-States <=50K
## 3898 United-States <=50K
## 3899 United-States <=50K
## 3900 United-States <=50K
## 3901 United-States >50K
## 3902 United-States <=50K
## 3903 United-States <=50K
## 3904 El-Salvador <=50K
## 3905 United-States <=50K
## 3906 United-States <=50K
## 3907 United-States <=50K
## 3908 United-States <=50K
## 3909 United-States <=50K
## 3910 Taiwan >50K
## 3911 United-States <=50K
## 3912 United-States >50K
## 3913 United-States <=50K
## 3914 United-States <=50K
## 3915 United-States <=50K
## 3916 United-States >50K
## 3917 United-States <=50K
## 3918 United-States <=50K
## 3919 United-States <=50K
## 3920 United-States <=50K
## 3921 United-States <=50K
## 3922 United-States <=50K
## 3923 United-States <=50K
## 3924 England <=50K
## 3925 Columbia <=50K
## 3926 United-States <=50K
## 3927 United-States >50K
## 3928 United-States >50K
## 3929 United-States <=50K
## 3930 United-States >50K
## 3931 United-States <=50K
## 3932 United-States <=50K
## 3933 United-States >50K
## 3934 United-States <=50K
## 3935 United-States <=50K
## 3936 United-States <=50K
## 3937 United-States <=50K
## 3938 United-States <=50K
## 3939 United-States <=50K
## 3940 United-States <=50K
## 3941 United-States <=50K
## 3942 Mexico <=50K
## 3943 United-States <=50K
## 3944 United-States <=50K
## 3945 Mexico <=50K
## 3946 United-States >50K
## 3947 England <=50K
## 3948 United-States <=50K
## 3949 United-States <=50K
## 3950 United-States <=50K
## 3951 United-States <=50K
## 3952 United-States <=50K
## 3953 United-States >50K
## 3954 United-States <=50K
## 3955 United-States <=50K
## 3956 Puerto-Rico <=50K
## 3957 United-States <=50K
## 3958 United-States <=50K
## 3959 United-States >50K
## 3960 United-States >50K
## 3961 United-States <=50K
## 3962 United-States <=50K
## 3963 England <=50K
## 3964 United-States <=50K
## 3965 United-States >50K
## 3966 United-States <=50K
## 3967 United-States >50K
## 3968 United-States <=50K
## 3969 United-States <=50K
## 3970 United-States <=50K
## 3971 United-States <=50K
## 3972 United-States <=50K
## 3973 Ireland <=50K
## 3974 Jamaica <=50K
## 3975 United-States <=50K
## 3976 United-States <=50K
## 3977 United-States <=50K
## 3978 United-States <=50K
## 3979 United-States <=50K
## 3980 United-States <=50K
## 3981 United-States >50K
## 3982 Ecuador <=50K
## 3983 Ireland <=50K
## 3984 United-States >50K
## 3985 United-States <=50K
## 3986 United-States <=50K
## 3987 United-States <=50K
## 3988 China <=50K
## 3989 United-States >50K
## 3990 United-States <=50K
## 3991 United-States <=50K
## 3992 United-States <=50K
## 3993 United-States >50K
## 3994 United-States <=50K
## 3995 United-States <=50K
## 3996 United-States <=50K
## 3997 United-States <=50K
## 3998 United-States <=50K
## 3999 United-States <=50K
## 4000 United-States <=50K
## 4001 United-States <=50K
## 4002 United-States <=50K
## 4003 United-States <=50K
## 4004 United-States <=50K
## 4005 United-States >50K
## 4006 United-States >50K
## 4007 ? >50K
## 4008 United-States <=50K
## 4009 United-States >50K
## 4010 United-States <=50K
## 4011 United-States <=50K
## 4012 United-States <=50K
## 4013 Mexico <=50K
## 4014 United-States <=50K
## 4015 United-States <=50K
## 4016 United-States <=50K
## 4017 United-States <=50K
## 4018 United-States <=50K
## 4019 United-States >50K
## 4020 United-States <=50K
## 4021 United-States <=50K
## 4022 United-States <=50K
## 4023 United-States <=50K
## 4024 United-States <=50K
## 4025 United-States >50K
## 4026 United-States <=50K
## 4027 United-States <=50K
## 4028 United-States <=50K
## 4029 United-States <=50K
## 4030 Greece <=50K
## 4031 United-States <=50K
## 4032 United-States >50K
## 4033 United-States >50K
## 4034 Jamaica <=50K
## 4035 United-States <=50K
## 4036 United-States >50K
## 4037 United-States <=50K
## 4038 United-States <=50K
## 4039 United-States <=50K
## 4040 United-States <=50K
## 4041 United-States <=50K
## 4042 United-States <=50K
## 4043 Poland >50K
## 4044 United-States <=50K
## 4045 United-States <=50K
## 4046 United-States <=50K
## 4047 United-States <=50K
## 4048 United-States <=50K
## 4049 United-States <=50K
## 4050 United-States <=50K
## 4051 United-States <=50K
## 4052 United-States <=50K
## 4053 United-States <=50K
## 4054 United-States >50K
## 4055 United-States <=50K
## 4056 Philippines >50K
## 4057 United-States >50K
## 4058 United-States <=50K
## 4059 United-States >50K
## 4060 United-States <=50K
## 4061 United-States >50K
## 4062 United-States <=50K
## 4063 United-States <=50K
## 4064 United-States <=50K
## 4065 United-States <=50K
## 4066 United-States <=50K
## 4067 South <=50K
## 4068 United-States <=50K
## 4069 United-States <=50K
## 4070 United-States <=50K
## 4071 United-States <=50K
## 4072 United-States <=50K
## 4073 United-States <=50K
## 4074 Germany <=50K
## 4075 United-States <=50K
## 4076 United-States >50K
## 4077 United-States <=50K
## 4078 United-States <=50K
## 4079 United-States <=50K
## 4080 United-States <=50K
## 4081 United-States <=50K
## 4082 United-States <=50K
## 4083 United-States <=50K
## 4084 United-States <=50K
## 4085 United-States <=50K
## 4086 Peru <=50K
## 4087 United-States <=50K
## 4088 United-States >50K
## 4089 United-States >50K
## 4090 United-States <=50K
## 4091 United-States <=50K
## 4092 United-States <=50K
## 4093 United-States <=50K
## 4094 United-States <=50K
## 4095 United-States <=50K
## 4096 United-States <=50K
## 4097 United-States >50K
## 4098 United-States >50K
## 4099 United-States <=50K
## 4100 United-States <=50K
## 4101 United-States <=50K
## 4102 United-States <=50K
## 4103 United-States <=50K
## 4104 United-States <=50K
## 4105 United-States <=50K
## 4106 United-States <=50K
## 4107 United-States <=50K
## 4108 El-Salvador <=50K
## 4109 Taiwan <=50K
## 4110 United-States <=50K
## 4111 United-States <=50K
## 4112 United-States <=50K
## 4113 United-States >50K
## 4114 United-States <=50K
## 4115 United-States <=50K
## 4116 United-States >50K
## 4117 United-States <=50K
## 4118 United-States <=50K
## 4119 United-States <=50K
## 4120 United-States <=50K
## 4121 United-States <=50K
## 4122 United-States >50K
## 4123 United-States <=50K
## 4124 United-States <=50K
## 4125 United-States <=50K
## 4126 United-States <=50K
## 4127 United-States <=50K
## 4128 United-States >50K
## 4129 United-States <=50K
## 4130 United-States <=50K
## 4131 El-Salvador <=50K
## 4132 United-States >50K
## 4133 United-States <=50K
## 4134 United-States >50K
## 4135 United-States <=50K
## 4136 United-States <=50K
## 4137 United-States <=50K
## 4138 United-States >50K
## 4139 Germany <=50K
## 4140 United-States <=50K
## 4141 United-States <=50K
## 4142 United-States <=50K
## 4143 United-States <=50K
## 4144 United-States >50K
## 4145 United-States <=50K
## 4146 Iran >50K
## 4147 United-States <=50K
## 4148 United-States <=50K
## 4149 United-States <=50K
## 4150 United-States <=50K
## 4151 United-States <=50K
## 4152 United-States <=50K
## 4153 United-States <=50K
## 4154 United-States <=50K
## 4155 United-States >50K
## 4156 United-States <=50K
## 4157 ? <=50K
## 4158 United-States <=50K
## 4159 Ecuador <=50K
## 4160 United-States <=50K
## 4161 United-States <=50K
## 4162 United-States <=50K
## 4163 United-States <=50K
## 4164 United-States <=50K
## 4165 United-States >50K
## 4166 United-States <=50K
## 4167 United-States <=50K
## 4168 United-States <=50K
## 4169 Vietnam <=50K
## 4170 United-States >50K
## 4171 United-States <=50K
## 4172 Dominican-Republic <=50K
## 4173 ? >50K
## 4174 United-States <=50K
## 4175 United-States <=50K
## 4176 United-States <=50K
## 4177 United-States >50K
## 4178 United-States >50K
## 4179 United-States <=50K
## 4180 United-States <=50K
## 4181 Haiti <=50K
## 4182 United-States <=50K
## 4183 Canada >50K
## 4184 United-States <=50K
## 4185 United-States <=50K
## 4186 United-States <=50K
## 4187 United-States <=50K
## 4188 United-States <=50K
## 4189 United-States <=50K
## 4190 United-States <=50K
## 4191 United-States <=50K
## 4192 United-States <=50K
## 4193 United-States <=50K
## 4194 United-States <=50K
## 4195 United-States <=50K
## 4196 United-States <=50K
## 4197 United-States <=50K
## 4198 ? <=50K
## 4199 United-States <=50K
## 4200 United-States <=50K
## 4201 United-States <=50K
## 4202 United-States <=50K
## 4203 United-States <=50K
## 4204 United-States >50K
## 4205 United-States <=50K
## 4206 United-States <=50K
## 4207 United-States <=50K
## 4208 United-States <=50K
## 4209 United-States >50K
## 4210 United-States <=50K
## 4211 United-States >50K
## 4212 United-States <=50K
## 4213 United-States <=50K
## 4214 United-States <=50K
## 4215 United-States <=50K
## 4216 United-States >50K
## 4217 United-States <=50K
## 4218 United-States <=50K
## 4219 El-Salvador <=50K
## 4220 United-States <=50K
## 4221 United-States <=50K
## 4222 United-States <=50K
## 4223 United-States >50K
## 4224 United-States <=50K
## 4225 United-States <=50K
## 4226 United-States <=50K
## 4227 United-States <=50K
## 4228 United-States <=50K
## 4229 United-States <=50K
## 4230 United-States <=50K
## 4231 United-States <=50K
## 4232 United-States <=50K
## 4233 United-States >50K
## 4234 Outlying-US(Guam-USVI-etc) <=50K
## 4235 United-States <=50K
## 4236 United-States <=50K
## 4237 United-States <=50K
## 4238 United-States <=50K
## 4239 United-States >50K
## 4240 United-States <=50K
## 4241 United-States <=50K
## 4242 United-States <=50K
## 4243 United-States <=50K
## 4244 United-States <=50K
## 4245 ? <=50K
## 4246 United-States <=50K
## 4247 United-States >50K
## 4248 United-States <=50K
## 4249 United-States <=50K
## 4250 United-States <=50K
## 4251 United-States <=50K
## 4252 United-States <=50K
## 4253 United-States <=50K
## 4254 United-States <=50K
## 4255 United-States <=50K
## 4256 United-States >50K
## 4257 United-States <=50K
## 4258 United-States >50K
## 4259 United-States >50K
## 4260 United-States <=50K
## 4261 United-States <=50K
## 4262 United-States <=50K
## 4263 Vietnam <=50K
## 4264 United-States <=50K
## 4265 United-States >50K
## 4266 United-States >50K
## 4267 United-States >50K
## 4268 United-States >50K
## 4269 United-States <=50K
## 4270 United-States <=50K
## 4271 United-States <=50K
## 4272 United-States >50K
## 4273 United-States <=50K
## 4274 United-States <=50K
## 4275 United-States <=50K
## 4276 United-States <=50K
## 4277 United-States <=50K
## 4278 United-States <=50K
## 4279 United-States <=50K
## 4280 United-States <=50K
## 4281 Mexico <=50K
## 4282 Puerto-Rico <=50K
## 4283 United-States <=50K
## 4284 United-States <=50K
## 4285 United-States <=50K
## 4286 United-States >50K
## 4287 Jamaica <=50K
## 4288 United-States <=50K
## 4289 United-States <=50K
## 4290 United-States <=50K
## 4291 United-States <=50K
## 4292 Mexico <=50K
## 4293 United-States <=50K
## 4294 United-States <=50K
## 4295 United-States <=50K
## 4296 United-States <=50K
## 4297 United-States <=50K
## 4298 United-States >50K
## 4299 United-States <=50K
## 4300 United-States <=50K
## 4301 United-States <=50K
## 4302 ? <=50K
## 4303 United-States <=50K
## 4304 Cambodia <=50K
## 4305 United-States >50K
## 4306 United-States <=50K
## 4307 United-States >50K
## 4308 United-States <=50K
## 4309 United-States <=50K
## 4310 United-States <=50K
## 4311 Ecuador <=50K
## 4312 United-States >50K
## 4313 United-States >50K
## 4314 United-States <=50K
## 4315 United-States <=50K
## 4316 Nicaragua <=50K
## 4317 United-States <=50K
## 4318 United-States <=50K
## 4319 United-States <=50K
## 4320 United-States <=50K
## 4321 United-States <=50K
## 4322 United-States <=50K
## 4323 United-States >50K
## 4324 United-States <=50K
## 4325 United-States <=50K
## 4326 Mexico <=50K
## 4327 ? <=50K
## 4328 United-States >50K
## 4329 United-States <=50K
## 4330 United-States >50K
## 4331 United-States >50K
## 4332 United-States <=50K
## 4333 Canada <=50K
## 4334 United-States <=50K
## 4335 United-States <=50K
## 4336 United-States <=50K
## 4337 United-States <=50K
## 4338 Philippines <=50K
## 4339 United-States >50K
## 4340 United-States >50K
## 4341 United-States <=50K
## 4342 United-States >50K
## 4343 United-States <=50K
## 4344 United-States >50K
## 4345 United-States >50K
## 4346 United-States <=50K
## 4347 United-States <=50K
## 4348 United-States <=50K
## 4349 United-States <=50K
## 4350 United-States >50K
## 4351 United-States <=50K
## 4352 United-States >50K
## 4353 United-States <=50K
## 4354 United-States <=50K
## 4355 Germany >50K
## 4356 United-States <=50K
## 4357 United-States <=50K
## 4358 United-States <=50K
## 4359 United-States <=50K
## 4360 United-States >50K
## 4361 United-States <=50K
## 4362 Jamaica <=50K
## 4363 United-States <=50K
## 4364 United-States <=50K
## 4365 United-States <=50K
## 4366 United-States >50K
## 4367 United-States >50K
## 4368 South <=50K
## 4369 United-States >50K
## 4370 Iran <=50K
## 4371 United-States <=50K
## 4372 United-States <=50K
## 4373 United-States <=50K
## 4374 United-States <=50K
## 4375 Puerto-Rico >50K
## 4376 United-States <=50K
## 4377 United-States <=50K
## 4378 United-States <=50K
## 4379 United-States >50K
## 4380 United-States <=50K
## 4381 United-States >50K
## 4382 Jamaica >50K
## 4383 United-States <=50K
## 4384 United-States <=50K
## 4385 Canada <=50K
## 4386 United-States <=50K
## 4387 United-States <=50K
## 4388 United-States <=50K
## 4389 United-States <=50K
## 4390 United-States >50K
## 4391 United-States >50K
## 4392 Mexico <=50K
## 4393 United-States <=50K
## 4394 United-States <=50K
## 4395 United-States <=50K
## 4396 United-States <=50K
## 4397 ? <=50K
## 4398 United-States <=50K
## 4399 United-States <=50K
## 4400 United-States <=50K
## 4401 United-States <=50K
## 4402 United-States >50K
## 4403 United-States <=50K
## 4404 Taiwan <=50K
## 4405 United-States >50K
## 4406 ? >50K
## 4407 United-States <=50K
## 4408 United-States >50K
## 4409 United-States <=50K
## 4410 South <=50K
## 4411 United-States <=50K
## 4412 United-States >50K
## 4413 United-States <=50K
## 4414 United-States <=50K
## 4415 United-States <=50K
## 4416 United-States <=50K
## 4417 United-States <=50K
## 4418 United-States >50K
## 4419 United-States <=50K
## 4420 United-States <=50K
## 4421 United-States <=50K
## 4422 United-States >50K
## 4423 United-States <=50K
## 4424 United-States <=50K
## 4425 United-States <=50K
## 4426 Canada <=50K
## 4427 United-States <=50K
## 4428 United-States <=50K
## 4429 Mexico <=50K
## 4430 United-States <=50K
## 4431 United-States <=50K
## 4432 United-States <=50K
## 4433 United-States <=50K
## 4434 Iran >50K
## 4435 United-States <=50K
## 4436 United-States <=50K
## 4437 United-States <=50K
## 4438 United-States >50K
## 4439 United-States >50K
## 4440 United-States <=50K
## 4441 United-States >50K
## 4442 United-States >50K
## 4443 United-States <=50K
## 4444 United-States >50K
## 4445 United-States <=50K
## 4446 United-States >50K
## 4447 United-States <=50K
## 4448 Yugoslavia <=50K
## 4449 United-States <=50K
## 4450 United-States <=50K
## 4451 United-States <=50K
## 4452 United-States <=50K
## 4453 United-States <=50K
## 4454 United-States <=50K
## 4455 United-States <=50K
## 4456 Italy <=50K
## 4457 Canada <=50K
## 4458 United-States <=50K
## 4459 United-States <=50K
## 4460 United-States <=50K
## 4461 United-States <=50K
## 4462 United-States <=50K
## 4463 ? <=50K
## 4464 United-States <=50K
## 4465 United-States <=50K
## 4466 United-States >50K
## 4467 United-States >50K
## 4468 United-States <=50K
## 4469 United-States <=50K
## 4470 United-States <=50K
## 4471 United-States >50K
## 4472 United-States >50K
## 4473 United-States <=50K
## 4474 United-States >50K
## 4475 United-States >50K
## 4476 United-States >50K
## 4477 United-States <=50K
## 4478 United-States <=50K
## 4479 United-States >50K
## 4480 United-States >50K
## 4481 India <=50K
## 4482 United-States <=50K
## 4483 United-States <=50K
## 4484 United-States <=50K
## 4485 United-States <=50K
## 4486 Jamaica <=50K
## 4487 United-States >50K
## 4488 Canada <=50K
## 4489 Canada <=50K
## 4490 United-States <=50K
## 4491 United-States <=50K
## 4492 United-States >50K
## 4493 United-States >50K
## 4494 United-States <=50K
## 4495 United-States <=50K
## 4496 United-States >50K
## 4497 United-States <=50K
## 4498 United-States <=50K
## 4499 United-States <=50K
## 4500 United-States <=50K
## 4501 United-States <=50K
## 4502 United-States <=50K
## 4503 United-States >50K
## 4504 United-States <=50K
## 4505 United-States <=50K
## 4506 United-States <=50K
## 4507 United-States <=50K
## 4508 United-States <=50K
## 4509 Outlying-US(Guam-USVI-etc) <=50K
## 4510 United-States <=50K
## 4511 ? <=50K
## 4512 United-States <=50K
## 4513 India <=50K
## 4514 United-States <=50K
## 4515 United-States <=50K
## 4516 United-States <=50K
## 4517 United-States <=50K
## 4518 United-States <=50K
## 4519 United-States >50K
## 4520 United-States <=50K
## 4521 United-States <=50K
## 4522 United-States <=50K
## 4523 United-States <=50K
## 4524 India <=50K
## 4525 United-States <=50K
## 4526 United-States <=50K
## 4527 United-States <=50K
## 4528 United-States <=50K
## 4529 United-States <=50K
## 4530 United-States <=50K
## 4531 United-States <=50K
## 4532 United-States <=50K
## 4533 Philippines <=50K
## 4534 United-States >50K
## 4535 United-States <=50K
## 4536 United-States <=50K
## 4537 Germany <=50K
## 4538 United-States >50K
## 4539 Philippines <=50K
## 4540 United-States >50K
## 4541 United-States >50K
## 4542 United-States <=50K
## 4543 United-States <=50K
## 4544 United-States >50K
## 4545 United-States <=50K
## 4546 United-States >50K
## 4547 United-States >50K
## 4548 United-States <=50K
## 4549 United-States <=50K
## 4550 United-States >50K
## 4551 United-States >50K
## 4552 United-States <=50K
## 4553 United-States <=50K
## 4554 United-States <=50K
## 4555 United-States <=50K
## 4556 United-States >50K
## 4557 United-States <=50K
## 4558 United-States <=50K
## 4559 United-States <=50K
## 4560 United-States >50K
## 4561 United-States <=50K
## 4562 United-States <=50K
## 4563 Mexico <=50K
## 4564 United-States <=50K
## 4565 United-States <=50K
## 4566 United-States <=50K
## 4567 United-States >50K
## 4568 United-States <=50K
## 4569 United-States <=50K
## 4570 United-States <=50K
## 4571 United-States <=50K
## 4572 United-States >50K
## 4573 United-States <=50K
## 4574 United-States <=50K
## 4575 United-States <=50K
## 4576 United-States >50K
## 4577 Trinadad&Tobago <=50K
## 4578 United-States <=50K
## 4579 ? <=50K
## 4580 United-States <=50K
## 4581 United-States <=50K
## 4582 United-States <=50K
## 4583 United-States <=50K
## 4584 United-States <=50K
## 4585 United-States >50K
## 4586 United-States <=50K
## 4587 United-States <=50K
## 4588 United-States <=50K
## 4589 United-States <=50K
## 4590 United-States <=50K
## 4591 United-States <=50K
## 4592 United-States <=50K
## 4593 United-States >50K
## 4594 United-States >50K
## 4595 United-States <=50K
## 4596 United-States >50K
## 4597 United-States <=50K
## 4598 United-States >50K
## 4599 China <=50K
## 4600 ? >50K
## 4601 United-States >50K
## 4602 United-States <=50K
## 4603 United-States <=50K
## 4604 United-States >50K
## 4605 United-States <=50K
## 4606 United-States <=50K
## 4607 United-States <=50K
## 4608 United-States <=50K
## 4609 United-States <=50K
## 4610 United-States <=50K
## 4611 Laos <=50K
## 4612 United-States <=50K
## 4613 United-States >50K
## 4614 United-States <=50K
## 4615 United-States <=50K
## 4616 United-States <=50K
## 4617 United-States <=50K
## 4618 United-States <=50K
## 4619 United-States >50K
## 4620 United-States <=50K
## 4621 United-States <=50K
## 4622 United-States <=50K
## 4623 United-States >50K
## 4624 United-States <=50K
## 4625 United-States <=50K
## 4626 Taiwan >50K
## 4627 United-States <=50K
## 4628 United-States <=50K
## 4629 United-States <=50K
## 4630 Canada >50K
## 4631 United-States <=50K
## 4632 United-States <=50K
## 4633 United-States >50K
## 4634 United-States <=50K
## 4635 United-States <=50K
## 4636 France <=50K
## 4637 United-States >50K
## 4638 United-States <=50K
## 4639 United-States <=50K
## 4640 ? >50K
## 4641 United-States <=50K
## 4642 United-States >50K
## 4643 Mexico <=50K
## 4644 United-States <=50K
## 4645 United-States <=50K
## 4646 United-States >50K
## 4647 United-States <=50K
## 4648 Jamaica <=50K
## 4649 United-States <=50K
## 4650 United-States <=50K
## 4651 United-States <=50K
## 4652 United-States <=50K
## 4653 United-States <=50K
## 4654 United-States <=50K
## 4655 United-States >50K
## 4656 United-States <=50K
## 4657 ? >50K
## 4658 United-States >50K
## 4659 ? <=50K
## 4660 United-States >50K
## 4661 United-States <=50K
## 4662 United-States >50K
## 4663 United-States <=50K
## 4664 United-States <=50K
## 4665 United-States <=50K
## 4666 United-States <=50K
## 4667 United-States <=50K
## 4668 United-States <=50K
## 4669 United-States <=50K
## 4670 United-States >50K
## 4671 United-States >50K
## 4672 ? <=50K
## 4673 United-States <=50K
## 4674 United-States <=50K
## 4675 United-States <=50K
## 4676 United-States >50K
## 4677 United-States <=50K
## 4678 United-States <=50K
## 4679 United-States <=50K
## 4680 United-States <=50K
## 4681 United-States >50K
## 4682 United-States <=50K
## 4683 United-States <=50K
## 4684 United-States <=50K
## 4685 United-States <=50K
## 4686 United-States >50K
## 4687 United-States <=50K
## 4688 United-States <=50K
## 4689 United-States <=50K
## 4690 United-States <=50K
## 4691 United-States <=50K
## 4692 Canada <=50K
## 4693 United-States >50K
## 4694 United-States <=50K
## 4695 United-States >50K
## 4696 United-States <=50K
## 4697 United-States <=50K
## 4698 Guatemala <=50K
## 4699 Mexico <=50K
## 4700 United-States <=50K
## 4701 Guatemala <=50K
## 4702 United-States <=50K
## 4703 United-States <=50K
## 4704 United-States <=50K
## 4705 Germany >50K
## 4706 Mexico <=50K
## 4707 United-States <=50K
## 4708 United-States <=50K
## 4709 United-States <=50K
## 4710 United-States >50K
## 4711 United-States <=50K
## 4712 United-States <=50K
## 4713 United-States <=50K
## 4714 United-States <=50K
## 4715 United-States <=50K
## 4716 United-States <=50K
## 4717 United-States <=50K
## 4718 United-States >50K
## 4719 Mexico <=50K
## 4720 United-States <=50K
## 4721 United-States <=50K
## 4722 United-States >50K
## 4723 Dominican-Republic <=50K
## 4724 United-States <=50K
## 4725 Vietnam <=50K
## 4726 United-States <=50K
## 4727 El-Salvador <=50K
## 4728 United-States >50K
## 4729 United-States <=50K
## 4730 United-States <=50K
## 4731 United-States <=50K
## 4732 United-States <=50K
## 4733 Mexico <=50K
## 4734 Mexico <=50K
## 4735 Mexico <=50K
## 4736 United-States <=50K
## 4737 United-States >50K
## 4738 United-States <=50K
## 4739 United-States <=50K
## 4740 United-States <=50K
## 4741 United-States >50K
## 4742 United-States <=50K
## 4743 United-States >50K
## 4744 United-States <=50K
## 4745 United-States <=50K
## 4746 United-States <=50K
## 4747 United-States >50K
## 4748 United-States >50K
## 4749 Germany <=50K
## 4750 United-States <=50K
## 4751 United-States >50K
## 4752 United-States <=50K
## 4753 United-States <=50K
## 4754 United-States >50K
## 4755 United-States <=50K
## 4756 United-States <=50K
## 4757 United-States <=50K
## 4758 United-States <=50K
## 4759 United-States >50K
## 4760 United-States <=50K
## 4761 United-States <=50K
## 4762 United-States >50K
## 4763 United-States <=50K
## 4764 United-States >50K
## 4765 United-States <=50K
## 4766 Ireland >50K
## 4767 United-States <=50K
## 4768 United-States <=50K
## 4769 Philippines >50K
## 4770 United-States <=50K
## 4771 United-States <=50K
## 4772 United-States <=50K
## 4773 ? >50K
## 4774 United-States <=50K
## 4775 United-States <=50K
## 4776 United-States <=50K
## 4777 United-States >50K
## 4778 United-States <=50K
## 4779 United-States <=50K
## 4780 United-States <=50K
## 4781 United-States >50K
## 4782 United-States <=50K
## 4783 United-States >50K
## 4784 United-States <=50K
## 4785 United-States >50K
## 4786 Cambodia <=50K
## 4787 ? <=50K
## 4788 United-States <=50K
## 4789 United-States <=50K
## 4790 United-States <=50K
## 4791 United-States <=50K
## 4792 United-States <=50K
## 4793 United-States <=50K
## 4794 United-States >50K
## 4795 United-States <=50K
## 4796 United-States <=50K
## 4797 United-States <=50K
## 4798 United-States >50K
## 4799 United-States <=50K
## 4800 United-States <=50K
## 4801 Mexico <=50K
## 4802 United-States <=50K
## 4803 United-States <=50K
## 4804 Philippines <=50K
## 4805 United-States <=50K
## 4806 United-States >50K
## 4807 United-States >50K
## 4808 Puerto-Rico >50K
## 4809 United-States >50K
## 4810 United-States <=50K
## 4811 United-States >50K
## 4812 United-States <=50K
## 4813 United-States >50K
## 4814 Italy <=50K
## 4815 United-States <=50K
## 4816 United-States <=50K
## 4817 United-States >50K
## 4818 United-States <=50K
## 4819 United-States <=50K
## 4820 United-States <=50K
## 4821 United-States <=50K
## 4822 United-States <=50K
## 4823 United-States <=50K
## 4824 United-States <=50K
## 4825 United-States <=50K
## 4826 United-States >50K
## 4827 Vietnam <=50K
## 4828 ? >50K
## 4829 United-States <=50K
## 4830 United-States <=50K
## 4831 Mexico <=50K
## 4832 United-States <=50K
## 4833 United-States >50K
## 4834 United-States >50K
## 4835 United-States <=50K
## 4836 United-States <=50K
## 4837 United-States >50K
## 4838 United-States <=50K
## 4839 United-States <=50K
## 4840 United-States <=50K
## 4841 United-States <=50K
## 4842 United-States <=50K
## 4843 United-States <=50K
## 4844 United-States <=50K
## 4845 United-States <=50K
## 4846 United-States <=50K
## 4847 United-States <=50K
## 4848 United-States >50K
## 4849 United-States <=50K
## 4850 United-States <=50K
## 4851 United-States >50K
## 4852 United-States <=50K
## 4853 United-States <=50K
## 4854 United-States <=50K
## 4855 Philippines <=50K
## 4856 United-States <=50K
## 4857 United-States <=50K
## 4858 United-States <=50K
## 4859 United-States >50K
## 4860 United-States <=50K
## 4861 United-States <=50K
## 4862 Dominican-Republic <=50K
## 4863 United-States >50K
## 4864 United-States <=50K
## 4865 United-States <=50K
## 4866 United-States >50K
## 4867 El-Salvador <=50K
## 4868 United-States <=50K
## 4869 United-States <=50K
## 4870 United-States <=50K
## 4871 United-States >50K
## 4872 United-States <=50K
## 4873 United-States >50K
## 4874 United-States >50K
## 4875 United-States >50K
## 4876 United-States >50K
## 4877 United-States <=50K
## 4878 United-States <=50K
## 4879 United-States >50K
## 4880 United-States <=50K
## 4881 United-States >50K
## 4882 Mexico <=50K
## 4883 United-States <=50K
## 4884 United-States <=50K
## 4885 United-States <=50K
## 4886 United-States >50K
## 4887 United-States <=50K
## 4888 United-States <=50K
## 4889 United-States >50K
## 4890 United-States <=50K
## 4891 United-States <=50K
## 4892 United-States <=50K
## 4893 United-States <=50K
## 4894 United-States <=50K
## 4895 United-States >50K
## 4896 United-States <=50K
## 4897 United-States <=50K
## 4898 United-States >50K
## 4899 United-States >50K
## 4900 United-States <=50K
## 4901 United-States >50K
## 4902 United-States <=50K
## 4903 United-States <=50K
## 4904 United-States >50K
## 4905 United-States <=50K
## 4906 Portugal >50K
## 4907 United-States >50K
## 4908 United-States <=50K
## 4909 United-States >50K
## 4910 United-States <=50K
## 4911 United-States <=50K
## 4912 United-States <=50K
## 4913 United-States <=50K
## 4914 Cuba <=50K
## 4915 United-States >50K
## 4916 United-States <=50K
## 4917 United-States <=50K
## 4918 United-States <=50K
## 4919 United-States <=50K
## 4920 Vietnam <=50K
## 4921 United-States <=50K
## 4922 United-States <=50K
## 4923 United-States <=50K
## 4924 United-States >50K
## 4925 United-States <=50K
## 4926 United-States <=50K
## 4927 United-States <=50K
## 4928 United-States <=50K
## 4929 United-States >50K
## 4930 United-States <=50K
## 4931 United-States <=50K
## 4932 United-States <=50K
## 4933 United-States >50K
## 4934 United-States <=50K
## 4935 United-States <=50K
## 4936 United-States >50K
## 4937 United-States <=50K
## 4938 United-States <=50K
## 4939 United-States <=50K
## 4940 United-States <=50K
## 4941 United-States >50K
## 4942 United-States >50K
## 4943 United-States <=50K
## 4944 United-States >50K
## 4945 United-States >50K
## 4946 United-States <=50K
## 4947 United-States <=50K
## 4948 United-States >50K
## 4949 United-States <=50K
## 4950 United-States >50K
## 4951 United-States >50K
## 4952 United-States >50K
## 4953 United-States >50K
## 4954 United-States >50K
## 4955 United-States <=50K
## 4956 United-States <=50K
## 4957 United-States <=50K
## 4958 United-States <=50K
## 4959 United-States <=50K
## 4960 United-States <=50K
## 4961 Puerto-Rico <=50K
## 4962 United-States >50K
## 4963 United-States <=50K
## 4964 United-States <=50K
## 4965 United-States <=50K
## 4966 United-States <=50K
## 4967 United-States <=50K
## 4968 United-States >50K
## 4969 United-States <=50K
## 4970 United-States <=50K
## 4971 United-States >50K
## 4972 United-States <=50K
## 4973 China >50K
## 4974 Mexico <=50K
## 4975 United-States >50K
## 4976 United-States <=50K
## 4977 United-States >50K
## 4978 United-States <=50K
## 4979 United-States >50K
## 4980 Guatemala <=50K
## 4981 United-States <=50K
## 4982 United-States <=50K
## 4983 United-States <=50K
## 4984 United-States >50K
## 4985 United-States <=50K
## 4986 United-States <=50K
## 4987 United-States <=50K
## 4988 United-States <=50K
## 4989 United-States >50K
## 4990 United-States <=50K
## 4991 United-States <=50K
## 4992 United-States <=50K
## 4993 United-States <=50K
## 4994 United-States <=50K
## 4995 United-States <=50K
## 4996 Mexico <=50K
## 4997 United-States >50K
## 4998 United-States >50K
## 4999 United-States <=50K
## 5000 United-States <=50K
## 5001 United-States >50K
## 5002 United-States <=50K
## 5003 United-States <=50K
## 5004 United-States <=50K
## 5005 United-States <=50K
## 5006 United-States <=50K
## 5007 United-States <=50K
## 5008 United-States >50K
## 5009 United-States <=50K
## 5010 United-States <=50K
## 5011 United-States <=50K
## 5012 United-States <=50K
## 5013 Japan <=50K
## 5014 United-States >50K
## 5015 United-States >50K
## 5016 United-States <=50K
## 5017 United-States <=50K
## 5018 United-States <=50K
## 5019 United-States <=50K
## 5020 Mexico <=50K
## 5021 United-States <=50K
## 5022 United-States <=50K
## 5023 United-States <=50K
## 5024 United-States <=50K
## 5025 United-States <=50K
## 5026 Cuba <=50K
## 5027 United-States >50K
## 5028 United-States >50K
## 5029 Mexico <=50K
## 5030 United-States <=50K
## 5031 Canada <=50K
## 5032 United-States >50K
## 5033 United-States <=50K
## 5034 United-States >50K
## 5035 United-States <=50K
## 5036 United-States <=50K
## 5037 United-States <=50K
## 5038 United-States <=50K
## 5039 United-States <=50K
## 5040 United-States <=50K
## 5041 United-States <=50K
## 5042 United-States <=50K
## 5043 United-States <=50K
## 5044 United-States <=50K
## 5045 United-States >50K
## 5046 United-States >50K
## 5047 United-States <=50K
## 5048 United-States <=50K
## 5049 Mexico <=50K
## 5050 United-States >50K
## 5051 United-States <=50K
## 5052 United-States >50K
## 5053 United-States <=50K
## 5054 United-States >50K
## 5055 United-States <=50K
## 5056 United-States <=50K
## 5057 United-States <=50K
## 5058 United-States <=50K
## 5059 United-States >50K
## 5060 United-States <=50K
## 5061 United-States <=50K
## 5062 United-States <=50K
## 5063 United-States >50K
## 5064 United-States <=50K
## 5065 United-States <=50K
## 5066 Mexico <=50K
## 5067 United-States <=50K
## 5068 United-States <=50K
## 5069 United-States <=50K
## 5070 United-States >50K
## 5071 United-States <=50K
## 5072 United-States <=50K
## 5073 United-States >50K
## 5074 United-States >50K
## 5075 United-States <=50K
## 5076 United-States >50K
## 5077 United-States <=50K
## 5078 United-States <=50K
## 5079 United-States <=50K
## 5080 United-States <=50K
## 5081 United-States <=50K
## 5082 ? <=50K
## 5083 United-States <=50K
## 5084 Jamaica <=50K
## 5085 United-States <=50K
## 5086 United-States >50K
## 5087 United-States <=50K
## 5088 United-States <=50K
## 5089 United-States >50K
## 5090 United-States <=50K
## 5091 United-States <=50K
## 5092 United-States <=50K
## 5093 Poland <=50K
## 5094 United-States <=50K
## 5095 United-States <=50K
## 5096 United-States <=50K
## 5097 United-States <=50K
## 5098 United-States <=50K
## 5099 United-States >50K
## 5100 United-States >50K
## 5101 United-States <=50K
## 5102 United-States >50K
## 5103 United-States <=50K
## 5104 United-States <=50K
## 5105 United-States <=50K
## 5106 United-States <=50K
## 5107 United-States <=50K
## 5108 United-States <=50K
## 5109 United-States >50K
## 5110 United-States <=50K
## 5111 United-States <=50K
## 5112 United-States <=50K
## 5113 Poland <=50K
## 5114 United-States <=50K
## 5115 United-States <=50K
## 5116 United-States <=50K
## 5117 United-States <=50K
## 5118 United-States <=50K
## 5119 Puerto-Rico <=50K
## 5120 United-States <=50K
## 5121 United-States <=50K
## 5122 United-States >50K
## 5123 United-States <=50K
## 5124 United-States <=50K
## 5125 United-States <=50K
## 5126 United-States <=50K
## 5127 United-States <=50K
## 5128 United-States <=50K
## 5129 United-States <=50K
## 5130 United-States <=50K
## 5131 United-States <=50K
## 5132 United-States <=50K
## 5133 Canada <=50K
## 5134 United-States <=50K
## 5135 United-States <=50K
## 5136 United-States <=50K
## 5137 United-States <=50K
## 5138 United-States >50K
## 5139 United-States <=50K
## 5140 United-States <=50K
## 5141 United-States >50K
## 5142 Canada <=50K
## 5143 United-States <=50K
## 5144 Germany <=50K
## 5145 United-States <=50K
## 5146 United-States <=50K
## 5147 United-States <=50K
## 5148 United-States <=50K
## 5149 United-States <=50K
## 5150 United-States <=50K
## 5151 United-States >50K
## 5152 United-States <=50K
## 5153 United-States <=50K
## 5154 United-States <=50K
## 5155 United-States <=50K
## 5156 United-States >50K
## 5157 United-States <=50K
## 5158 United-States <=50K
## 5159 United-States >50K
## 5160 United-States <=50K
## 5161 United-States >50K
## 5162 United-States <=50K
## 5163 United-States <=50K
## 5164 United-States <=50K
## 5165 United-States >50K
## 5166 United-States <=50K
## 5167 United-States >50K
## 5168 United-States >50K
## 5169 United-States <=50K
## 5170 United-States >50K
## 5171 United-States <=50K
## 5172 United-States <=50K
## 5173 United-States >50K
## 5174 United-States >50K
## 5175 United-States <=50K
## 5176 United-States >50K
## 5177 United-States >50K
## 5178 United-States <=50K
## 5179 United-States <=50K
## 5180 United-States >50K
## 5181 ? <=50K
## 5182 United-States <=50K
## 5183 United-States <=50K
## 5184 United-States <=50K
## 5185 United-States >50K
## 5186 ? <=50K
## 5187 United-States <=50K
## 5188 United-States <=50K
## 5189 United-States <=50K
## 5190 United-States <=50K
## 5191 United-States <=50K
## 5192 United-States <=50K
## 5193 United-States >50K
## 5194 United-States <=50K
## 5195 Cuba >50K
## 5196 United-States <=50K
## 5197 Taiwan >50K
## 5198 United-States <=50K
## 5199 United-States >50K
## 5200 United-States <=50K
## 5201 United-States >50K
## 5202 ? >50K
## 5203 United-States >50K
## 5204 United-States <=50K
## 5205 United-States <=50K
## 5206 Iran <=50K
## 5207 United-States <=50K
## 5208 United-States <=50K
## 5209 United-States <=50K
## 5210 United-States <=50K
## 5211 United-States <=50K
## 5212 United-States >50K
## 5213 United-States <=50K
## 5214 United-States <=50K
## 5215 United-States <=50K
## 5216 United-States <=50K
## 5217 United-States <=50K
## 5218 United-States <=50K
## 5219 United-States <=50K
## 5220 Taiwan <=50K
## 5221 United-States >50K
## 5222 United-States <=50K
## 5223 United-States >50K
## 5224 United-States <=50K
## 5225 United-States >50K
## 5226 United-States <=50K
## 5227 United-States <=50K
## 5228 United-States >50K
## 5229 United-States <=50K
## 5230 United-States <=50K
## 5231 United-States <=50K
## 5232 United-States >50K
## 5233 United-States <=50K
## 5234 United-States <=50K
## 5235 ? <=50K
## 5236 United-States <=50K
## 5237 United-States >50K
## 5238 United-States <=50K
## 5239 United-States >50K
## 5240 United-States <=50K
## 5241 United-States <=50K
## 5242 Cuba <=50K
## 5243 United-States <=50K
## 5244 United-States <=50K
## 5245 United-States <=50K
## 5246 United-States <=50K
## 5247 Canada <=50K
## 5248 United-States <=50K
## 5249 United-States <=50K
## 5250 United-States <=50K
## 5251 United-States <=50K
## 5252 United-States >50K
## 5253 United-States <=50K
## 5254 United-States <=50K
## 5255 Mexico <=50K
## 5256 United-States <=50K
## 5257 United-States <=50K
## 5258 Mexico <=50K
## 5259 United-States <=50K
## 5260 Scotland <=50K
## 5261 United-States <=50K
## 5262 United-States <=50K
## 5263 United-States <=50K
## 5264 United-States <=50K
## 5265 United-States >50K
## 5266 United-States <=50K
## 5267 United-States <=50K
## 5268 United-States >50K
## 5269 United-States <=50K
## 5270 United-States >50K
## 5271 United-States <=50K
## 5272 United-States <=50K
## 5273 United-States <=50K
## 5274 United-States <=50K
## 5275 United-States >50K
## 5276 United-States >50K
## 5277 United-States >50K
## 5278 United-States <=50K
## 5279 Mexico <=50K
## 5280 United-States <=50K
## 5281 United-States <=50K
## 5282 United-States <=50K
## 5283 United-States >50K
## 5284 United-States <=50K
## 5285 United-States <=50K
## 5286 United-States >50K
## 5287 El-Salvador <=50K
## 5288 United-States >50K
## 5289 United-States <=50K
## 5290 United-States <=50K
## 5291 United-States >50K
## 5292 United-States <=50K
## 5293 United-States <=50K
## 5294 United-States <=50K
## 5295 United-States <=50K
## 5296 United-States <=50K
## 5297 United-States <=50K
## 5298 United-States <=50K
## 5299 United-States <=50K
## 5300 United-States <=50K
## 5301 United-States <=50K
## 5302 Poland >50K
## 5303 United-States <=50K
## 5304 United-States <=50K
## 5305 United-States <=50K
## 5306 United-States <=50K
## 5307 United-States <=50K
## 5308 United-States <=50K
## 5309 United-States <=50K
## 5310 ? >50K
## 5311 United-States <=50K
## 5312 United-States >50K
## 5313 United-States <=50K
## 5314 United-States <=50K
## 5315 United-States <=50K
## 5316 United-States <=50K
## 5317 United-States <=50K
## 5318 United-States <=50K
## 5319 United-States <=50K
## 5320 United-States <=50K
## 5321 United-States <=50K
## 5322 United-States <=50K
## 5323 United-States <=50K
## 5324 United-States <=50K
## 5325 United-States <=50K
## 5326 Mexico <=50K
## 5327 United-States <=50K
## 5328 United-States >50K
## 5329 United-States <=50K
## 5330 United-States >50K
## 5331 South <=50K
## 5332 Cuba <=50K
## 5333 United-States <=50K
## 5334 United-States <=50K
## 5335 United-States <=50K
## 5336 United-States <=50K
## 5337 United-States <=50K
## 5338 United-States >50K
## 5339 United-States <=50K
## 5340 United-States <=50K
## 5341 Mexico <=50K
## 5342 United-States <=50K
## 5343 United-States <=50K
## 5344 United-States <=50K
## 5345 United-States <=50K
## 5346 United-States <=50K
## 5347 Germany <=50K
## 5348 ? >50K
## 5349 United-States <=50K
## 5350 United-States >50K
## 5351 United-States <=50K
## 5352 United-States >50K
## 5353 United-States <=50K
## 5354 United-States <=50K
## 5355 United-States <=50K
## 5356 United-States <=50K
## 5357 United-States <=50K
## 5358 United-States <=50K
## 5359 United-States >50K
## 5360 United-States <=50K
## 5361 United-States <=50K
## 5362 United-States <=50K
## 5363 United-States <=50K
## 5364 United-States <=50K
## 5365 United-States <=50K
## 5366 United-States <=50K
## 5367 United-States <=50K
## 5368 United-States <=50K
## 5369 United-States >50K
## 5370 United-States <=50K
## 5371 United-States >50K
## 5372 United-States <=50K
## 5373 United-States <=50K
## 5374 United-States <=50K
## 5375 ? >50K
## 5376 United-States <=50K
## 5377 United-States <=50K
## 5378 United-States >50K
## 5379 United-States <=50K
## 5380 United-States <=50K
## 5381 United-States >50K
## 5382 United-States <=50K
## 5383 United-States <=50K
## 5384 United-States <=50K
## 5385 United-States <=50K
## 5386 Puerto-Rico >50K
## 5387 United-States <=50K
## 5388 United-States <=50K
## 5389 Germany >50K
## 5390 United-States <=50K
## 5391 United-States <=50K
## 5392 United-States <=50K
## 5393 United-States <=50K
## 5394 United-States <=50K
## 5395 United-States <=50K
## 5396 Vietnam <=50K
## 5397 United-States <=50K
## 5398 United-States >50K
## 5399 United-States <=50K
## 5400 United-States <=50K
## 5401 United-States >50K
## 5402 ? <=50K
## 5403 United-States <=50K
## 5404 United-States <=50K
## 5405 United-States <=50K
## 5406 United-States >50K
## 5407 United-States >50K
## 5408 United-States >50K
## 5409 United-States >50K
## 5410 United-States <=50K
## 5411 United-States <=50K
## 5412 United-States <=50K
## 5413 United-States <=50K
## 5414 United-States <=50K
## 5415 United-States >50K
## 5416 Vietnam <=50K
## 5417 United-States <=50K
## 5418 United-States <=50K
## 5419 United-States >50K
## 5420 United-States <=50K
## 5421 United-States <=50K
## 5422 United-States <=50K
## 5423 United-States <=50K
## 5424 United-States <=50K
## 5425 South <=50K
## 5426 United-States >50K
## 5427 United-States <=50K
## 5428 United-States <=50K
## 5429 United-States <=50K
## 5430 United-States >50K
## 5431 United-States <=50K
## 5432 United-States >50K
## 5433 United-States <=50K
## 5434 United-States <=50K
## 5435 United-States <=50K
## 5436 China <=50K
## 5437 United-States >50K
## 5438 United-States >50K
## 5439 United-States <=50K
## 5440 United-States <=50K
## 5441 United-States <=50K
## 5442 Italy <=50K
## 5443 United-States <=50K
## 5444 United-States <=50K
## 5445 United-States <=50K
## 5446 United-States <=50K
## 5447 United-States <=50K
## 5448 United-States <=50K
## 5449 United-States <=50K
## 5450 United-States <=50K
## 5451 ? <=50K
## 5452 United-States <=50K
## 5453 El-Salvador <=50K
## 5454 United-States <=50K
## 5455 United-States <=50K
## 5456 United-States <=50K
## 5457 United-States >50K
## 5458 United-States >50K
## 5459 United-States >50K
## 5460 United-States <=50K
## 5461 United-States <=50K
## 5462 United-States <=50K
## 5463 United-States >50K
## 5464 Columbia <=50K
## 5465 United-States <=50K
## 5466 United-States <=50K
## 5467 United-States <=50K
## 5468 United-States <=50K
## 5469 United-States <=50K
## 5470 United-States <=50K
## 5471 United-States <=50K
## 5472 Poland <=50K
## 5473 United-States <=50K
## 5474 United-States >50K
## 5475 United-States <=50K
## 5476 United-States <=50K
## 5477 United-States <=50K
## 5478 United-States <=50K
## 5479 United-States >50K
## 5480 United-States >50K
## 5481 United-States >50K
## 5482 United-States <=50K
## 5483 United-States <=50K
## 5484 United-States <=50K
## 5485 United-States <=50K
## 5486 United-States <=50K
## 5487 United-States <=50K
## 5488 United-States >50K
## 5489 United-States >50K
## 5490 United-States <=50K
## 5491 United-States >50K
## 5492 Japan <=50K
## 5493 England <=50K
## 5494 United-States <=50K
## 5495 United-States >50K
## 5496 United-States <=50K
## 5497 United-States <=50K
## 5498 United-States >50K
## 5499 United-States <=50K
## 5500 United-States <=50K
## 5501 United-States <=50K
## 5502 United-States <=50K
## 5503 United-States <=50K
## 5504 Mexico <=50K
## 5505 United-States <=50K
## 5506 United-States <=50K
## 5507 United-States <=50K
## 5508 United-States >50K
## 5509 United-States <=50K
## 5510 United-States <=50K
## 5511 Dominican-Republic <=50K
## 5512 United-States <=50K
## 5513 United-States <=50K
## 5514 England <=50K
## 5515 United-States >50K
## 5516 United-States <=50K
## 5517 United-States <=50K
## 5518 United-States <=50K
## 5519 United-States >50K
## 5520 United-States <=50K
## 5521 United-States <=50K
## 5522 United-States >50K
## 5523 United-States <=50K
## 5524 United-States >50K
## 5525 United-States <=50K
## 5526 Philippines <=50K
## 5527 United-States <=50K
## 5528 United-States <=50K
## 5529 United-States <=50K
## 5530 United-States <=50K
## 5531 United-States >50K
## 5532 United-States <=50K
## 5533 United-States >50K
## 5534 United-States <=50K
## 5535 United-States <=50K
## 5536 United-States <=50K
## 5537 United-States <=50K
## 5538 United-States >50K
## 5539 United-States >50K
## 5540 United-States <=50K
## 5541 ? <=50K
## 5542 United-States <=50K
## 5543 United-States >50K
## 5544 United-States >50K
## 5545 United-States <=50K
## 5546 United-States <=50K
## 5547 United-States <=50K
## 5548 United-States >50K
## 5549 United-States <=50K
## 5550 United-States <=50K
## 5551 United-States <=50K
## 5552 United-States <=50K
## 5553 United-States <=50K
## 5554 United-States <=50K
## 5555 United-States <=50K
## 5556 United-States >50K
## 5557 United-States <=50K
## 5558 United-States <=50K
## 5559 United-States <=50K
## 5560 United-States <=50K
## 5561 Cambodia >50K
## 5562 United-States <=50K
## 5563 United-States <=50K
## 5564 United-States <=50K
## 5565 United-States <=50K
## 5566 United-States <=50K
## 5567 United-States >50K
## 5568 United-States <=50K
## 5569 United-States >50K
## 5570 United-States <=50K
## 5571 United-States >50K
## 5572 United-States <=50K
## 5573 United-States <=50K
## 5574 United-States <=50K
## 5575 United-States >50K
## 5576 United-States <=50K
## 5577 United-States <=50K
## 5578 United-States <=50K
## 5579 United-States <=50K
## 5580 United-States <=50K
## 5581 United-States >50K
## 5582 United-States >50K
## 5583 United-States >50K
## 5584 United-States <=50K
## 5585 United-States >50K
## 5586 United-States <=50K
## 5587 United-States <=50K
## 5588 United-States <=50K
## 5589 United-States >50K
## 5590 United-States >50K
## 5591 United-States <=50K
## 5592 United-States <=50K
## 5593 South <=50K
## 5594 United-States >50K
## 5595 United-States <=50K
## 5596 United-States >50K
## 5597 United-States <=50K
## 5598 United-States <=50K
## 5599 United-States <=50K
## 5600 United-States >50K
## 5601 United-States >50K
## 5602 United-States >50K
## 5603 United-States >50K
## 5604 United-States <=50K
## 5605 Philippines <=50K
## 5606 United-States <=50K
## 5607 United-States >50K
## 5608 United-States <=50K
## 5609 United-States <=50K
## 5610 United-States <=50K
## 5611 United-States >50K
## 5612 United-States >50K
## 5613 United-States >50K
## 5614 United-States <=50K
## 5615 United-States >50K
## 5616 United-States <=50K
## 5617 United-States <=50K
## 5618 Poland <=50K
## 5619 United-States <=50K
## 5620 Ireland <=50K
## 5621 United-States >50K
## 5622 United-States <=50K
## 5623 United-States <=50K
## 5624 United-States <=50K
## 5625 United-States >50K
## 5626 United-States >50K
## 5627 United-States <=50K
## 5628 United-States <=50K
## 5629 United-States >50K
## 5630 India <=50K
## 5631 United-States <=50K
## 5632 United-States <=50K
## 5633 United-States <=50K
## 5634 United-States <=50K
## 5635 United-States >50K
## 5636 United-States <=50K
## 5637 Puerto-Rico <=50K
## 5638 United-States <=50K
## 5639 United-States <=50K
## 5640 United-States >50K
## 5641 United-States <=50K
## 5642 United-States <=50K
## 5643 United-States <=50K
## 5644 United-States >50K
## 5645 United-States <=50K
## 5646 United-States <=50K
## 5647 United-States <=50K
## 5648 ? <=50K
## 5649 United-States <=50K
## 5650 United-States >50K
## 5651 United-States <=50K
## 5652 United-States >50K
## 5653 United-States <=50K
## 5654 United-States <=50K
## 5655 United-States <=50K
## 5656 United-States <=50K
## 5657 United-States <=50K
## 5658 United-States >50K
## 5659 United-States >50K
## 5660 United-States <=50K
## 5661 United-States >50K
## 5662 United-States <=50K
## 5663 United-States <=50K
## 5664 ? >50K
## 5665 Puerto-Rico <=50K
## 5666 United-States <=50K
## 5667 United-States >50K
## 5668 United-States <=50K
## 5669 United-States >50K
## 5670 United-States >50K
## 5671 United-States <=50K
## 5672 United-States <=50K
## 5673 United-States >50K
## 5674 United-States <=50K
## 5675 United-States >50K
## 5676 United-States <=50K
## 5677 United-States <=50K
## 5678 United-States <=50K
## 5679 United-States <=50K
## 5680 United-States <=50K
## 5681 United-States <=50K
## 5682 United-States <=50K
## 5683 United-States >50K
## 5684 ? <=50K
## 5685 United-States <=50K
## 5686 United-States >50K
## 5687 United-States <=50K
## 5688 United-States <=50K
## 5689 United-States >50K
## 5690 United-States <=50K
## 5691 United-States <=50K
## 5692 United-States <=50K
## 5693 United-States <=50K
## 5694 United-States <=50K
## 5695 United-States >50K
## 5696 United-States <=50K
## 5697 United-States >50K
## 5698 United-States <=50K
## 5699 Philippines <=50K
## 5700 United-States <=50K
## 5701 United-States <=50K
## 5702 United-States <=50K
## 5703 United-States >50K
## 5704 United-States >50K
## 5705 United-States <=50K
## 5706 Germany <=50K
## 5707 United-States <=50K
## 5708 United-States <=50K
## 5709 United-States >50K
## 5710 ? >50K
## 5711 United-States >50K
## 5712 United-States >50K
## 5713 United-States >50K
## 5714 United-States >50K
## 5715 United-States <=50K
## 5716 United-States <=50K
## 5717 Guatemala <=50K
## 5718 United-States >50K
## 5719 United-States <=50K
## 5720 United-States <=50K
## 5721 Germany <=50K
## 5722 United-States <=50K
## 5723 United-States <=50K
## 5724 United-States <=50K
## 5725 United-States <=50K
## 5726 United-States <=50K
## 5727 United-States <=50K
## 5728 United-States >50K
## 5729 United-States >50K
## 5730 United-States <=50K
## 5731 United-States <=50K
## 5732 United-States <=50K
## 5733 United-States <=50K
## 5734 United-States <=50K
## 5735 Portugal <=50K
## 5736 United-States <=50K
## 5737 United-States <=50K
## 5738 United-States <=50K
## 5739 United-States <=50K
## 5740 Mexico <=50K
## 5741 Mexico <=50K
## 5742 Puerto-Rico <=50K
## 5743 United-States <=50K
## 5744 United-States <=50K
## 5745 United-States <=50K
## 5746 United-States <=50K
## 5747 United-States <=50K
## 5748 United-States >50K
## 5749 United-States >50K
## 5750 United-States <=50K
## 5751 United-States >50K
## 5752 United-States <=50K
## 5753 United-States <=50K
## 5754 United-States <=50K
## 5755 Canada >50K
## 5756 United-States >50K
## 5757 United-States >50K
## 5758 United-States >50K
## 5759 United-States <=50K
## 5760 United-States <=50K
## 5761 United-States <=50K
## 5762 United-States <=50K
## 5763 United-States <=50K
## 5764 United-States <=50K
## 5765 United-States >50K
## 5766 United-States <=50K
## 5767 United-States <=50K
## 5768 United-States >50K
## 5769 United-States <=50K
## 5770 United-States <=50K
## 5771 United-States <=50K
## 5772 Philippines >50K
## 5773 United-States <=50K
## 5774 United-States <=50K
## 5775 United-States <=50K
## 5776 United-States <=50K
## 5777 United-States <=50K
## 5778 United-States <=50K
## 5779 United-States <=50K
## 5780 United-States <=50K
## 5781 United-States <=50K
## 5782 United-States <=50K
## 5783 United-States <=50K
## 5784 United-States <=50K
## 5785 United-States <=50K
## 5786 United-States <=50K
## 5787 China <=50K
## 5788 England <=50K
## 5789 United-States <=50K
## 5790 United-States <=50K
## 5791 United-States <=50K
## 5792 United-States >50K
## 5793 United-States >50K
## 5794 United-States >50K
## 5795 United-States >50K
## 5796 United-States <=50K
## 5797 Germany >50K
## 5798 United-States <=50K
## 5799 United-States >50K
## 5800 United-States <=50K
## 5801 United-States <=50K
## 5802 United-States <=50K
## 5803 United-States <=50K
## 5804 United-States <=50K
## 5805 United-States <=50K
## 5806 United-States <=50K
## 5807 United-States <=50K
## 5808 United-States <=50K
## 5809 United-States <=50K
## 5810 United-States <=50K
## 5811 Mexico <=50K
## 5812 United-States <=50K
## 5813 Haiti <=50K
## 5814 United-States >50K
## 5815 United-States <=50K
## 5816 United-States <=50K
## 5817 United-States <=50K
## 5818 United-States >50K
## 5819 United-States <=50K
## 5820 United-States <=50K
## 5821 United-States <=50K
## 5822 United-States >50K
## 5823 United-States >50K
## 5824 ? <=50K
## 5825 United-States <=50K
## 5826 United-States <=50K
## 5827 United-States <=50K
## 5828 United-States <=50K
## 5829 United-States <=50K
## 5830 United-States <=50K
## 5831 United-States <=50K
## 5832 United-States >50K
## 5833 United-States <=50K
## 5834 United-States <=50K
## 5835 Cuba <=50K
## 5836 United-States <=50K
## 5837 United-States >50K
## 5838 United-States <=50K
## 5839 United-States <=50K
## 5840 Greece >50K
## 5841 United-States <=50K
## 5842 ? <=50K
## 5843 Guatemala <=50K
## 5844 United-States <=50K
## 5845 United-States <=50K
## 5846 United-States <=50K
## 5847 United-States <=50K
## 5848 United-States <=50K
## 5849 United-States >50K
## 5850 United-States <=50K
## 5851 United-States <=50K
## 5852 United-States <=50K
## 5853 United-States <=50K
## 5854 United-States <=50K
## 5855 ? <=50K
## 5856 United-States <=50K
## 5857 United-States <=50K
## 5858 United-States >50K
## 5859 United-States <=50K
## 5860 United-States <=50K
## 5861 Mexico <=50K
## 5862 United-States <=50K
## 5863 United-States <=50K
## 5864 United-States <=50K
## 5865 United-States <=50K
## 5866 United-States <=50K
## 5867 United-States <=50K
## 5868 United-States >50K
## 5869 United-States <=50K
## 5870 United-States <=50K
## 5871 Dominican-Republic <=50K
## 5872 United-States >50K
## 5873 United-States <=50K
## 5874 United-States <=50K
## 5875 United-States <=50K
## 5876 United-States >50K
## 5877 United-States >50K
## 5878 Mexico <=50K
## 5879 United-States <=50K
## 5880 United-States <=50K
## 5881 United-States <=50K
## 5882 United-States <=50K
## 5883 United-States >50K
## 5884 Greece <=50K
## 5885 United-States <=50K
## 5886 United-States <=50K
## 5887 United-States <=50K
## 5888 United-States <=50K
## 5889 United-States <=50K
## 5890 United-States >50K
## 5891 United-States <=50K
## 5892 United-States <=50K
## 5893 United-States <=50K
## 5894 United-States <=50K
## 5895 United-States <=50K
## 5896 United-States <=50K
## 5897 United-States <=50K
## 5898 United-States <=50K
## 5899 United-States >50K
## 5900 El-Salvador <=50K
## 5901 United-States <=50K
## 5902 United-States <=50K
## 5903 United-States <=50K
## 5904 United-States <=50K
## 5905 United-States <=50K
## 5906 United-States <=50K
## 5907 United-States <=50K
## 5908 United-States <=50K
## 5909 United-States <=50K
## 5910 United-States >50K
## 5911 United-States <=50K
## 5912 Greece <=50K
## 5913 United-States <=50K
## 5914 United-States <=50K
## 5915 United-States <=50K
## 5916 United-States <=50K
## 5917 United-States <=50K
## 5918 United-States >50K
## 5919 United-States <=50K
## 5920 United-States >50K
## 5921 United-States <=50K
## 5922 United-States <=50K
## 5923 United-States <=50K
## 5924 United-States <=50K
## 5925 United-States <=50K
## 5926 United-States >50K
## 5927 United-States <=50K
## 5928 United-States <=50K
## 5929 United-States <=50K
## 5930 United-States >50K
## 5931 United-States <=50K
## 5932 United-States >50K
## 5933 United-States <=50K
## 5934 United-States <=50K
## 5935 United-States <=50K
## 5936 United-States <=50K
## 5937 United-States >50K
## 5938 United-States >50K
## 5939 United-States <=50K
## 5940 United-States <=50K
## 5941 United-States >50K
## 5942 United-States >50K
## 5943 United-States <=50K
## 5944 United-States >50K
## 5945 United-States >50K
## 5946 United-States <=50K
## 5947 United-States <=50K
## 5948 United-States <=50K
## 5949 United-States >50K
## 5950 United-States <=50K
## 5951 United-States <=50K
## 5952 United-States <=50K
## 5953 United-States <=50K
## 5954 United-States <=50K
## 5955 United-States <=50K
## 5956 United-States >50K
## 5957 United-States <=50K
## 5958 United-States <=50K
## 5959 Mexico <=50K
## 5960 United-States <=50K
## 5961 United-States <=50K
## 5962 United-States <=50K
## 5963 United-States <=50K
## 5964 ? <=50K
## 5965 United-States <=50K
## 5966 United-States >50K
## 5967 United-States <=50K
## 5968 United-States <=50K
## 5969 Canada >50K
## 5970 United-States <=50K
## 5971 United-States >50K
## 5972 Laos <=50K
## 5973 United-States <=50K
## 5974 United-States <=50K
## 5975 United-States <=50K
## 5976 Mexico <=50K
## 5977 United-States <=50K
## 5978 United-States >50K
## 5979 United-States <=50K
## 5980 United-States >50K
## 5981 Japan >50K
## 5982 United-States <=50K
## 5983 United-States <=50K
## 5984 United-States <=50K
## 5985 United-States <=50K
## 5986 United-States >50K
## 5987 United-States <=50K
## 5988 United-States <=50K
## 5989 United-States <=50K
## 5990 United-States <=50K
## 5991 United-States <=50K
## 5992 United-States <=50K
## 5993 United-States <=50K
## 5994 United-States <=50K
## 5995 United-States <=50K
## 5996 United-States <=50K
## 5997 United-States <=50K
## 5998 Mexico >50K
## 5999 United-States <=50K
## 6000 United-States <=50K
## 6001 United-States <=50K
## 6002 United-States <=50K
## 6003 United-States >50K
## 6004 United-States <=50K
## 6005 United-States >50K
## 6006 ? >50K
## 6007 Philippines <=50K
## 6008 United-States >50K
## 6009 United-States >50K
## 6010 United-States <=50K
## 6011 United-States >50K
## 6012 United-States >50K
## 6013 United-States <=50K
## 6014 United-States <=50K
## 6015 United-States <=50K
## 6016 United-States <=50K
## 6017 United-States >50K
## 6018 United-States >50K
## 6019 United-States <=50K
## 6020 United-States <=50K
## 6021 United-States >50K
## 6022 United-States <=50K
## 6023 United-States <=50K
## 6024 United-States <=50K
## 6025 United-States >50K
## 6026 United-States >50K
## 6027 United-States <=50K
## 6028 United-States <=50K
## 6029 United-States <=50K
## 6030 United-States <=50K
## 6031 United-States >50K
## 6032 United-States <=50K
## 6033 United-States >50K
## 6034 United-States <=50K
## 6035 United-States <=50K
## 6036 United-States >50K
## 6037 United-States >50K
## 6038 United-States <=50K
## 6039 Japan <=50K
## 6040 United-States <=50K
## 6041 Mexico <=50K
## 6042 United-States <=50K
## 6043 United-States >50K
## 6044 Poland <=50K
## 6045 Jamaica <=50K
## 6046 United-States >50K
## 6047 United-States <=50K
## 6048 United-States <=50K
## 6049 United-States <=50K
## 6050 United-States <=50K
## 6051 Greece <=50K
## 6052 United-States <=50K
## 6053 United-States <=50K
## 6054 United-States <=50K
## 6055 United-States <=50K
## 6056 United-States <=50K
## 6057 United-States <=50K
## 6058 Mexico <=50K
## 6059 United-States <=50K
## 6060 ? <=50K
## 6061 United-States <=50K
## 6062 United-States <=50K
## 6063 United-States <=50K
## 6064 United-States <=50K
## 6065 United-States <=50K
## 6066 China <=50K
## 6067 United-States >50K
## 6068 Mexico <=50K
## 6069 United-States <=50K
## 6070 United-States <=50K
## 6071 United-States <=50K
## 6072 United-States <=50K
## 6073 United-States <=50K
## 6074 United-States <=50K
## 6075 United-States <=50K
## 6076 United-States <=50K
## 6077 United-States <=50K
## 6078 United-States <=50K
## 6079 United-States <=50K
## 6080 United-States <=50K
## 6081 United-States <=50K
## 6082 United-States <=50K
## 6083 United-States >50K
## 6084 United-States <=50K
## 6085 United-States >50K
## 6086 United-States <=50K
## 6087 United-States <=50K
## 6088 United-States <=50K
## 6089 United-States <=50K
## 6090 United-States <=50K
## 6091 United-States <=50K
## 6092 United-States <=50K
## 6093 United-States <=50K
## 6094 United-States <=50K
## 6095 United-States <=50K
## 6096 United-States <=50K
## 6097 United-States <=50K
## 6098 United-States <=50K
## 6099 United-States <=50K
## 6100 United-States <=50K
## 6101 United-States <=50K
## 6102 United-States >50K
## 6103 United-States <=50K
## 6104 United-States <=50K
## 6105 United-States <=50K
## 6106 United-States >50K
## 6107 United-States <=50K
## 6108 United-States <=50K
## 6109 United-States <=50K
## 6110 United-States <=50K
## 6111 United-States <=50K
## 6112 United-States >50K
## 6113 United-States >50K
## 6114 United-States <=50K
## 6115 United-States <=50K
## 6116 United-States >50K
## 6117 United-States >50K
## 6118 United-States <=50K
## 6119 United-States <=50K
## 6120 United-States <=50K
## 6121 United-States >50K
## 6122 United-States >50K
## 6123 United-States <=50K
## 6124 United-States <=50K
## 6125 United-States >50K
## 6126 Philippines <=50K
## 6127 United-States <=50K
## 6128 United-States <=50K
## 6129 United-States <=50K
## 6130 ? <=50K
## 6131 United-States <=50K
## 6132 Mexico <=50K
## 6133 United-States <=50K
## 6134 United-States <=50K
## 6135 United-States <=50K
## 6136 England >50K
## 6137 United-States <=50K
## 6138 United-States >50K
## 6139 United-States <=50K
## 6140 United-States <=50K
## 6141 United-States <=50K
## 6142 United-States <=50K
## 6143 United-States <=50K
## 6144 United-States >50K
## 6145 United-States <=50K
## 6146 United-States <=50K
## 6147 United-States <=50K
## 6148 United-States >50K
## 6149 United-States <=50K
## 6150 United-States >50K
## 6151 South <=50K
## 6152 United-States <=50K
## 6153 United-States <=50K
## 6154 United-States <=50K
## 6155 United-States <=50K
## 6156 United-States <=50K
## 6157 United-States <=50K
## 6158 United-States <=50K
## 6159 United-States <=50K
## 6160 United-States <=50K
## 6161 United-States <=50K
## 6162 United-States <=50K
## 6163 United-States <=50K
## 6164 United-States <=50K
## 6165 United-States <=50K
## 6166 United-States <=50K
## 6167 United-States <=50K
## 6168 United-States <=50K
## 6169 United-States <=50K
## 6170 United-States <=50K
## 6171 United-States >50K
## 6172 United-States <=50K
## 6173 United-States <=50K
## 6174 El-Salvador >50K
## 6175 United-States <=50K
## 6176 Vietnam <=50K
## 6177 ? <=50K
## 6178 United-States >50K
## 6179 United-States <=50K
## 6180 United-States >50K
## 6181 United-States <=50K
## 6182 United-States >50K
## 6183 Iran <=50K
## 6184 United-States <=50K
## 6185 United-States >50K
## 6186 United-States <=50K
## 6187 ? >50K
## 6188 United-States <=50K
## 6189 United-States <=50K
## 6190 United-States <=50K
## 6191 United-States <=50K
## 6192 United-States <=50K
## 6193 United-States >50K
## 6194 United-States <=50K
## 6195 United-States <=50K
## 6196 United-States <=50K
## 6197 United-States >50K
## 6198 United-States >50K
## 6199 United-States <=50K
## 6200 United-States >50K
## 6201 United-States >50K
## 6202 United-States <=50K
## 6203 United-States <=50K
## 6204 Puerto-Rico <=50K
## 6205 United-States <=50K
## 6206 United-States >50K
## 6207 United-States <=50K
## 6208 United-States <=50K
## 6209 United-States >50K
## 6210 United-States <=50K
## 6211 United-States <=50K
## 6212 United-States <=50K
## 6213 Jamaica >50K
## 6214 United-States >50K
## 6215 United-States <=50K
## 6216 United-States <=50K
## 6217 United-States >50K
## 6218 United-States >50K
## 6219 United-States <=50K
## 6220 India <=50K
## 6221 United-States >50K
## 6222 United-States <=50K
## 6223 United-States <=50K
## 6224 United-States <=50K
## 6225 United-States >50K
## 6226 United-States >50K
## 6227 United-States <=50K
## 6228 United-States >50K
## 6229 United-States <=50K
## 6230 United-States <=50K
## 6231 United-States <=50K
## 6232 United-States <=50K
## 6233 United-States <=50K
## 6234 United-States >50K
## 6235 United-States <=50K
## 6236 United-States <=50K
## 6237 United-States >50K
## 6238 United-States <=50K
## 6239 United-States <=50K
## 6240 United-States <=50K
## 6241 United-States <=50K
## 6242 United-States <=50K
## 6243 ? <=50K
## 6244 United-States <=50K
## 6245 United-States <=50K
## 6246 United-States >50K
## 6247 Mexico <=50K
## 6248 United-States <=50K
## 6249 United-States <=50K
## 6250 United-States <=50K
## 6251 United-States >50K
## 6252 United-States <=50K
## 6253 United-States <=50K
## 6254 United-States <=50K
## 6255 United-States <=50K
## 6256 United-States <=50K
## 6257 United-States >50K
## 6258 United-States <=50K
## 6259 United-States <=50K
## 6260 United-States <=50K
## 6261 United-States <=50K
## 6262 United-States <=50K
## 6263 United-States <=50K
## 6264 United-States <=50K
## 6265 United-States >50K
## 6266 United-States <=50K
## 6267 United-States <=50K
## 6268 United-States <=50K
## 6269 United-States <=50K
## 6270 Philippines <=50K
## 6271 United-States <=50K
## 6272 United-States <=50K
## 6273 United-States <=50K
## 6274 United-States <=50K
## 6275 Mexico <=50K
## 6276 United-States <=50K
## 6277 United-States <=50K
## 6278 United-States <=50K
## 6279 United-States <=50K
## 6280 United-States <=50K
## 6281 United-States <=50K
## 6282 United-States >50K
## 6283 United-States <=50K
## 6284 United-States <=50K
## 6285 United-States <=50K
## 6286 United-States <=50K
## 6287 United-States <=50K
## 6288 United-States <=50K
## 6289 Jamaica <=50K
## 6290 United-States >50K
## 6291 United-States <=50K
## 6292 United-States >50K
## 6293 United-States <=50K
## 6294 United-States <=50K
## 6295 United-States <=50K
## 6296 Iran >50K
## 6297 United-States <=50K
## 6298 United-States <=50K
## 6299 United-States <=50K
## 6300 United-States >50K
## 6301 United-States <=50K
## 6302 United-States <=50K
## 6303 United-States <=50K
## 6304 United-States >50K
## 6305 United-States >50K
## 6306 United-States <=50K
## 6307 United-States <=50K
## 6308 United-States <=50K
## 6309 United-States <=50K
## 6310 United-States <=50K
## 6311 United-States >50K
## 6312 United-States <=50K
## 6313 United-States <=50K
## 6314 United-States >50K
## 6315 Philippines <=50K
## 6316 United-States <=50K
## 6317 United-States <=50K
## 6318 United-States <=50K
## 6319 Germany <=50K
## 6320 ? <=50K
## 6321 United-States <=50K
## 6322 India <=50K
## 6323 United-States <=50K
## 6324 United-States <=50K
## 6325 United-States >50K
## 6326 United-States >50K
## 6327 United-States <=50K
## 6328 United-States <=50K
## 6329 Yugoslavia <=50K
## 6330 United-States <=50K
## 6331 United-States >50K
## 6332 Puerto-Rico >50K
## 6333 United-States >50K
## 6334 United-States <=50K
## 6335 United-States >50K
## 6336 United-States <=50K
## 6337 United-States <=50K
## 6338 Canada >50K
## 6339 United-States <=50K
## 6340 United-States <=50K
## 6341 United-States <=50K
## 6342 United-States <=50K
## 6343 United-States <=50K
## 6344 United-States <=50K
## 6345 Germany >50K
## 6346 United-States <=50K
## 6347 United-States <=50K
## 6348 United-States <=50K
## 6349 United-States <=50K
## 6350 United-States >50K
## 6351 United-States <=50K
## 6352 United-States <=50K
## 6353 United-States >50K
## 6354 United-States <=50K
## 6355 United-States <=50K
## 6356 United-States <=50K
## 6357 United-States <=50K
## 6358 United-States <=50K
## 6359 United-States <=50K
## 6360 United-States >50K
## 6361 ? <=50K
## 6362 United-States <=50K
## 6363 United-States <=50K
## 6364 United-States <=50K
## 6365 ? <=50K
## 6366 United-States <=50K
## 6367 United-States <=50K
## 6368 United-States <=50K
## 6369 United-States <=50K
## 6370 United-States >50K
## 6371 United-States <=50K
## 6372 United-States <=50K
## 6373 United-States <=50K
## 6374 United-States >50K
## 6375 United-States <=50K
## 6376 United-States <=50K
## 6377 ? <=50K
## 6378 United-States <=50K
## 6379 United-States >50K
## 6380 United-States >50K
## 6381 United-States <=50K
## 6382 United-States <=50K
## 6383 United-States >50K
## 6384 United-States >50K
## 6385 United-States >50K
## 6386 United-States <=50K
## 6387 United-States <=50K
## 6388 China <=50K
## 6389 United-States <=50K
## 6390 United-States <=50K
## 6391 United-States <=50K
## 6392 United-States >50K
## 6393 United-States >50K
## 6394 Mexico <=50K
## 6395 United-States <=50K
## 6396 ? <=50K
## 6397 United-States <=50K
## 6398 United-States <=50K
## 6399 United-States <=50K
## 6400 United-States <=50K
## 6401 United-States <=50K
## 6402 United-States <=50K
## 6403 United-States <=50K
## 6404 United-States <=50K
## 6405 United-States <=50K
## 6406 United-States <=50K
## 6407 United-States >50K
## 6408 United-States <=50K
## 6409 United-States <=50K
## 6410 United-States <=50K
## 6411 United-States <=50K
## 6412 United-States <=50K
## 6413 United-States <=50K
## 6414 United-States <=50K
## 6415 United-States >50K
## 6416 United-States >50K
## 6417 United-States >50K
## 6418 United-States <=50K
## 6419 United-States <=50K
## 6420 United-States >50K
## 6421 United-States >50K
## 6422 Haiti <=50K
## 6423 Puerto-Rico <=50K
## 6424 United-States <=50K
## 6425 Greece <=50K
## 6426 United-States <=50K
## 6427 United-States >50K
## 6428 United-States <=50K
## 6429 United-States <=50K
## 6430 United-States >50K
## 6431 United-States <=50K
## 6432 United-States <=50K
## 6433 United-States <=50K
## 6434 Mexico <=50K
## 6435 United-States <=50K
## 6436 United-States <=50K
## 6437 United-States <=50K
## 6438 United-States <=50K
## 6439 United-States <=50K
## 6440 United-States <=50K
## 6441 United-States <=50K
## 6442 United-States <=50K
## 6443 United-States >50K
## 6444 United-States <=50K
## 6445 United-States <=50K
## 6446 United-States <=50K
## 6447 United-States <=50K
## 6448 United-States <=50K
## 6449 United-States <=50K
## 6450 United-States <=50K
## 6451 United-States <=50K
## 6452 United-States <=50K
## 6453 United-States <=50K
## 6454 United-States >50K
## 6455 United-States <=50K
## 6456 United-States >50K
## 6457 United-States <=50K
## 6458 United-States >50K
## 6459 United-States <=50K
## 6460 United-States <=50K
## 6461 Puerto-Rico <=50K
## 6462 United-States <=50K
## 6463 United-States <=50K
## 6464 United-States <=50K
## 6465 United-States <=50K
## 6466 United-States >50K
## 6467 United-States <=50K
## 6468 United-States <=50K
## 6469 United-States >50K
## 6470 United-States <=50K
## 6471 United-States <=50K
## 6472 United-States <=50K
## 6473 United-States <=50K
## 6474 United-States <=50K
## 6475 United-States <=50K
## 6476 United-States >50K
## 6477 United-States <=50K
## 6478 United-States <=50K
## 6479 United-States >50K
## 6480 United-States <=50K
## 6481 United-States <=50K
## 6482 United-States <=50K
## 6483 United-States >50K
## 6484 United-States >50K
## 6485 United-States >50K
## 6486 Mexico >50K
## 6487 United-States <=50K
## 6488 United-States >50K
## 6489 United-States <=50K
## 6490 United-States <=50K
## 6491 United-States <=50K
## 6492 United-States <=50K
## 6493 United-States >50K
## 6494 South <=50K
## 6495 United-States <=50K
## 6496 United-States <=50K
## 6497 United-States <=50K
## 6498 United-States <=50K
## 6499 United-States <=50K
## 6500 United-States >50K
## 6501 United-States >50K
## 6502 United-States <=50K
## 6503 United-States <=50K
## 6504 United-States <=50K
## 6505 United-States <=50K
## 6506 Philippines <=50K
## 6507 United-States <=50K
## 6508 United-States <=50K
## 6509 United-States <=50K
## 6510 United-States <=50K
## 6511 United-States <=50K
## 6512 United-States <=50K
## 6513 United-States >50K
## 6514 United-States >50K
## 6515 United-States <=50K
## 6516 United-States <=50K
## 6517 United-States <=50K
## 6518 United-States <=50K
## 6519 United-States <=50K
## 6520 United-States >50K
## 6521 United-States <=50K
## 6522 Mexico <=50K
## 6523 United-States <=50K
## 6524 United-States <=50K
## 6525 Mexico >50K
## 6526 United-States <=50K
## 6527 United-States <=50K
## 6528 Iran <=50K
## 6529 United-States >50K
## 6530 United-States <=50K
## 6531 United-States >50K
## 6532 United-States <=50K
## 6533 Canada <=50K
## 6534 ? >50K
## 6535 United-States <=50K
## 6536 United-States >50K
## 6537 United-States <=50K
## 6538 United-States >50K
## 6539 United-States <=50K
## 6540 United-States <=50K
## 6541 United-States <=50K
## 6542 United-States <=50K
## 6543 United-States <=50K
## 6544 United-States <=50K
## 6545 United-States <=50K
## 6546 United-States <=50K
## 6547 United-States >50K
## 6548 United-States >50K
## 6549 United-States >50K
## 6550 Taiwan <=50K
## 6551 United-States <=50K
## 6552 United-States <=50K
## 6553 United-States <=50K
## 6554 United-States <=50K
## 6555 United-States <=50K
## 6556 United-States >50K
## 6557 United-States >50K
## 6558 United-States <=50K
## 6559 United-States <=50K
## 6560 Italy <=50K
## 6561 India <=50K
## 6562 United-States >50K
## 6563 United-States <=50K
## 6564 United-States >50K
## 6565 United-States <=50K
## 6566 United-States <=50K
## 6567 United-States <=50K
## 6568 United-States <=50K
## 6569 United-States <=50K
## 6570 United-States >50K
## 6571 South <=50K
## 6572 United-States <=50K
## 6573 United-States <=50K
## 6574 United-States >50K
## 6575 United-States >50K
## 6576 United-States <=50K
## 6577 United-States >50K
## 6578 United-States <=50K
## 6579 Jamaica <=50K
## 6580 United-States <=50K
## 6581 Germany >50K
## 6582 United-States >50K
## 6583 United-States <=50K
## 6584 Guatemala <=50K
## 6585 United-States <=50K
## 6586 United-States <=50K
## 6587 United-States <=50K
## 6588 United-States <=50K
## 6589 United-States <=50K
## 6590 United-States <=50K
## 6591 Italy >50K
## 6592 United-States <=50K
## 6593 United-States <=50K
## 6594 United-States <=50K
## 6595 United-States <=50K
## 6596 United-States >50K
## 6597 United-States <=50K
## 6598 United-States <=50K
## 6599 United-States >50K
## 6600 United-States >50K
## 6601 United-States <=50K
## 6602 Jamaica <=50K
## 6603 United-States <=50K
## 6604 United-States >50K
## 6605 Iran <=50K
## 6606 United-States <=50K
## 6607 Italy >50K
## 6608 United-States <=50K
## 6609 United-States >50K
## 6610 United-States >50K
## 6611 United-States <=50K
## 6612 United-States <=50K
## 6613 United-States <=50K
## 6614 Mexico <=50K
## 6615 United-States >50K
## 6616 United-States <=50K
## 6617 United-States <=50K
## 6618 United-States <=50K
## 6619 United-States <=50K
## 6620 United-States <=50K
## 6621 Mexico <=50K
## 6622 United-States >50K
## 6623 United-States <=50K
## 6624 United-States <=50K
## 6625 United-States <=50K
## 6626 United-States >50K
## 6627 United-States <=50K
## 6628 United-States <=50K
## 6629 United-States <=50K
## 6630 United-States <=50K
## 6631 United-States <=50K
## 6632 South <=50K
## 6633 United-States <=50K
## 6634 United-States >50K
## 6635 Jamaica <=50K
## 6636 Greece <=50K
## 6637 United-States <=50K
## 6638 United-States >50K
## 6639 United-States <=50K
## 6640 United-States >50K
## 6641 United-States <=50K
## 6642 United-States <=50K
## 6643 United-States <=50K
## 6644 United-States <=50K
## 6645 United-States <=50K
## 6646 United-States <=50K
## 6647 United-States <=50K
## 6648 United-States <=50K
## 6649 United-States >50K
## 6650 United-States >50K
## 6651 United-States <=50K
## 6652 United-States <=50K
## 6653 United-States >50K
## 6654 United-States <=50K
## 6655 United-States <=50K
## 6656 Jamaica >50K
## 6657 United-States <=50K
## 6658 United-States <=50K
## 6659 United-States >50K
## 6660 United-States <=50K
## 6661 United-States >50K
## 6662 United-States >50K
## 6663 United-States <=50K
## 6664 United-States <=50K
## 6665 United-States >50K
## 6666 United-States >50K
## [ reached 'max' / getOption("max.print") -- omitted 25896 rows ]## age workclass similar_pop_count
## Min. :17.00 Private :22696 Min. : 12285
## 1st Qu.:28.00 Self-emp-not-inc: 2541 1st Qu.: 117827
## Median :37.00 Local-gov : 2093 Median : 178356
## Mean :38.58 ? : 1836 Mean : 189778
## 3rd Qu.:48.00 State-gov : 1298 3rd Qu.: 237051
## Max. :90.00 Self-emp-inc : 1116 Max. :1484705
## NA's :1 (Other) : 982 NA's :1
## education_level years_education marital_status
## HS-grad :10501 Min. : 1.00 Married-civ-spouse :14976
## Some-college: 7291 1st Qu.: 9.00 Never-married :10683
## Bachelors : 5355 Median :10.00 Divorced : 4443
## Masters : 1723 Mean :10.08 Separated : 1025
## Assoc-voc : 1382 3rd Qu.:12.00 Widowed : 993
## 11th : 1175 Max. :16.00 Married-spouse-absent: 418
## (Other) : 5135 NA's :1 (Other) : 24
## occupation household_role race
## Prof-specialty :4140 : 1 : 1
## Craft-repair :4099 Husband :13193 Amer-Indian-Eskimo: 311
## Exec-managerial:4066 Not-in-family : 8305 Asian-Pac-Islander: 1039
## Adm-clerical :3770 Other-relative: 981 Black : 3124
## Sales :3650 Own-child : 5068 Other : 271
## Other-service :3295 Unmarried : 3446 White :27816
## (Other) :9542 Wife : 1568
## gender capital_gain capital_loss hours_per_week
## : 1 Min. : 0 Min. : 0.0 Min. : 1.00
## Female:10771 1st Qu.: 0 1st Qu.: 0.0 1st Qu.:40.00
## Male :21790 Median : 0 Median : 0.0 Median :40.00
## Mean : 1078 Mean : 87.3 Mean :40.44
## 3rd Qu.: 0 3rd Qu.: 0.0 3rd Qu.:45.00
## Max. :99999 Max. :4356.0 Max. :99.00
## NA's :1 NA's :1 NA's :1
## native_region income
## United-States:29170 : 1
## Mexico : 643 <=50K:24720
## ? : 583 >50K : 7841
## Philippines : 198
## Germany : 137
## Canada : 121
## (Other) : 1710Tidying Data and resolving missing values:
- First get a card of missing values per column and then count them. ** Findings: Of 32562 data values 15 are missing (less than 0.1 percent missing).
#Checking data types
str(adult)## 'data.frame': 32562 obs. of 15 variables:
## $ age : int 39 50 38 53 28 37 49 52 31 42 ...
## $ workclass : Factor w/ 10 levels ""," ?"," Federal-gov",..: 9 8 6 6 6 6 6 8 6 6 ...
## $ similar_pop_count: int 77516 83311 215646 234721 338409 284582 160187 209642 45781 159449 ...
## $ education_level : Factor w/ 17 levels ""," 10th"," 11th",..: 11 11 13 3 11 14 8 13 14 11 ...
## $ years_education : int 13 13 9 7 13 14 5 9 14 13 ...
## $ marital_status : Factor w/ 8 levels ""," Divorced",..: 6 4 2 4 4 4 5 4 6 4 ...
## $ occupation : Factor w/ 16 levels ""," ?"," Adm-clerical",..: 3 6 8 8 12 6 10 6 12 6 ...
## $ household_role : Factor w/ 7 levels ""," Husband",..: 3 2 3 2 7 7 3 2 3 2 ...
## $ race : Factor w/ 6 levels ""," Amer-Indian-Eskimo",..: 6 6 6 4 4 6 4 6 6 6 ...
## $ gender : Factor w/ 3 levels ""," Female"," Male": 3 3 3 3 2 2 2 3 2 3 ...
## $ capital_gain : int 2174 0 0 0 0 0 0 0 14084 5178 ...
## $ capital_loss : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hours_per_week : int 40 13 40 40 40 40 16 45 50 40 ...
## $ native_region : Factor w/ 43 levels ""," ?"," Cambodia",..: 41 41 41 41 7 41 25 41 41 41 ...
## $ income : Factor w/ 3 levels ""," <=50K"," >50K": 2 2 2 2 2 2 2 3 3 3 ...cat("\n------------------Count NA----------------------\n\n")##
## ------------------Count NA----------------------sort(colSums(is.na(adult)), decreasing = TRUE)## age similar_pop_count years_education capital_gain
## 1 1 1 1
## capital_loss hours_per_week workclass education_level
## 1 1 0 0
## marital_status occupation household_role race
## 0 0 0 0
## gender native_region income
## 0 0 0vis_miss(adult)
dim(adult)## [1] 32562 15Most of the variables are categorical. To Better analyze them, we will:
- Reclassified values into value groups to reduce levels ahead of any EDA and predictions.
- replaced the remaining 15 missing values with averages.
# Function to recode the values with trimmed spaces
recode_value <- function(old_level) {
old_level <- trimws(old_level) # Trim leading and trailing whitespace
for (category in names(new_levels)) {
if (old_level %in% new_levels[[category]]) {
return(category)
}
}
return(NA) # Return NA if the education level does not match any category
}
levels(adult$workclass)<- c("unemployed", "unemployed","FedGov","LocGov","NeverWorked","Private",
"SelfEmpInc","SelfEmpNotInc","StateGov","unemployed")
# The new education level categories
new_levels <- list(
no_edu = c("", "Preschool"),
primary = c("1st-4th", "5th-6th"),
secondary = c("7th-8th"),
highsch = c("9th", "10th", "11th", "12th", "HS-grad"),
assoc = c("Assoc-acdm", "Assoc-voc", "Some-college"),
undergrad = c("Bachelors", "Prof-school"),
master = c("Masters"),
phd = c("Doctorate")
)
# Recode the education levels with the updated function
adult$education_level <- sapply(adult$education_level, recode_value)
# recreate the levels
levels(adult$education_level)<- c("no_edu", "primary", "secondary", "highsch", "assoc", "undergrad", "master", "phd")
# The new marital status categories
new_levels <- list(
divorce = c("Divorced", "Separated"),
married = c("Married-AF-spouse", "Married-civ-spouse", "Married-spouse-absent"),
notmarried = c("", "Never-married"),
widowed = c("Widowed")
)
# Recode the marital statuses with the updated function
adult$marital_status <- sapply(adult$marital_status, recode_value)
# recreate the levels
levels(adult$marital_status)<- c("divorce", "married", "notmarried", "widowed")
new_levels <- list(
other = c("", "?"),
clerical = c("Adm-clerical"),
midskill = c("Craft-repair", "Machine-op-inspct", "Transport-moving"),
lowskill = c("Handlers-cleaners", "Other-service", "Priv-house-serv", "Armed-Forces"),
highskill = c("Sales", "Tech-support", "Protective-serv", "Prof-specialty", "Exec-managerial"),
agriculture = c("Farming-fishing")
)
# Recode the marital statuses with the updated function
adult$occupation <- sapply(adult$occupation, recode_value)
# recreate the levels
levels(adult$occupation)<- c("other", "clerical", "midskill", "lowskill", "highskill", "agriculture")
# Define the new levels for household_role
new_levels <- list(
husband = c("Husband"),
wife = c("Wife"),
tenant = c("Not-in-family"),
single_occ = c("Unmarried", ""),
relative = c("Other-relative"),
depchild = c("Own-child")
)
# Recode the household_role column using the recode function
adult$household_role <- sapply(adult$household_role, recode_value)
# Convert the recoded household_role to a factor
adult$household_role <- factor(adult$household_role, levels = names(new_levels))
# Define the new levels for native_country
new_levels <- list(
EAsia = c("Vietnam", "Laos", "Cambodia", "Thailand", "China", "Hong", "Taiwan", "Philippines", "Japan"),
SAsia = c("India", "Iran"),
NorthAmerica = c("Canada", "Mexico", "United-States"),
CentrAmerica = c("Cuba", "Dominican-Republic", "Guatemala", "Haiti", "Honduras", "Jamaica", "Trinadad&Tobago", "Nicaragua", "El-Salvador", "", "?"),
SouthAmerica = c("Ecuador", "Peru", "Columbia", "South"),
Europe = c("France", "Germany", "Greece", "Holand-Netherlands", "Italy", "Hungary", "Ireland", "Poland", "Portugal", "Scotland", "England", "Yugoslavia"),
USTerritory = c("Outlying-US(Guam-USVI-etc)", "Puerto-Rico")
)
# Recode the native_country column using the recode function
adult$native_region <- sapply(adult$native_region, recode_value)
# Convert the recoded native_country to a factor
adult$native_region <- factor(adult$native_region)
# Define the new levels for income
new_levels <- list(
"<=50K" = c("<=50K", ""),
">50K" = c(">50K")
)
# Recode the income column using the recode function
adult$income <- sapply(adult$income, recode_value)
# Convert the recoded income to a factor
adult$income <- factor(adult$income, levels = names(new_levels))
levels(adult$income)## [1] "<=50K" ">50K"# Define the new levels for gender
new_levels <- list(
"female" = c("Female", ""),
"male" = c("Male")
)
# Recode the gender column using the recode function
adult$gender <- sapply(adult$gender, recode_value)
# Convert the recoded gender to a factor
adult$gender <- factor(adult$gender, levels = names(new_levels))
levels(adult$gender)## [1] "female" "male"# Define the new levels for race
new_levels <- list(
other = c("Other", ""),
white = c("White"),
black = c("Black"),
native = c("Amer-Indian-Eskimo"),
Asian_Pac_Islander = c("Asian-Pac-Islander")
)
# Recode the race column using the recode function
adult$race <- sapply(adult$race, recode_value)
# Convert the recoded race to a factor
adult$race <- factor(adult$race, levels = names(new_levels))
levels(adult$race)## [1] "other" "white" "black"
## [4] "native" "Asian_Pac_Islander"# get the average of all remaining missing values
replace_missing_with_average <- function(data) {
for (col_name in names(data)) {
# Check if the column is numeric
if (is.numeric(data[[col_name]])) {
# Calculate the average, excluding NA values
column_mean <- mean(data[[col_name]], na.rm = TRUE)
# Replace NA values with the column's average
data[[col_name]][is.na(data[[col_name]])] <- column_mean
}
}
return(data)
}
# Call the replace function:
adult <- replace_missing_with_average(adult)
# Make sure categorical variables are factored
adult[] <- lapply(adult, function(x) if(is.character(x)) as.factor(x) else x)
# Create the spans in a new column
adult <- adult %>%
mutate(hours_worked_span = case_when(
`hours_per_week` >= 1 & `hours_per_week` <= 15 ~ "1-15",
`hours_per_week` >= 16 & `hours_per_week` <= 30 ~ "16-30",
`hours_per_week` >= 31 & `hours_per_week` <= 45 ~ "31-45",
`hours_per_week` >= 46 & `hours_per_week` <= 60 ~ "46-60",
`hours_per_week` >= 61 & `hours_per_week` <= 75 ~ "61-75",
`hours_per_week` >= 76 & `hours_per_week` <= 100 ~ "76-100",
TRUE ~ NA_character_ # Handle any other cases
))
# Convert the recoded hours_worked_span to a factor
adult$hours_worked_span <- factor(adult$hours_worked_span, levels = c("1-15", "16-30", "31-45", "46-60", "61-75", "76-100"))
# Check the levels
levels(adult$hours_worked_span)## [1] "1-15" "16-30" "31-45" "46-60" "61-75" "76-100"# Create new spans and a column
adult <- adult %>%
mutate(age_span = cut(age, breaks = c(0, 20, 35, 50, 65, 75, 100),
labels = c("1-20", "21-35", "36-50", "51-65", "66-75", "76-100"),
right = FALSE))
# Convert the 'age_span' column to a categorical type with ordered levels
adult$age_span <- factor(adult$age_span,
levels = c("1-20", "21-35", "36-50", "51-65", "66-75", "76-100"),
ordered = TRUE)
# Check the levels of the new factor
levels(adult$age_span)## [1] "1-20" "21-35" "36-50" "51-65" "66-75" "76-100"# Review changes
adult## age workclass similar_pop_count education_level years_education
## 1 39 StateGov 77516 undergrad 13
## 2 50 SelfEmpNotInc 83311 undergrad 13
## 3 38 Private 215646 highsch 9
## 4 53 Private 234721 highsch 7
## 5 28 Private 338409 undergrad 13
## 6 37 Private 284582 master 14
## 7 49 Private 160187 highsch 5
## 8 52 SelfEmpNotInc 209642 highsch 9
## 9 31 Private 45781 master 14
## 10 42 Private 159449 undergrad 13
## 11 37 Private 280464 assoc 10
## 12 30 StateGov 141297 undergrad 13
## 13 23 Private 122272 undergrad 13
## 14 32 Private 205019 assoc 12
## 15 40 Private 121772 assoc 11
## 16 34 Private 245487 secondary 4
## 17 25 SelfEmpNotInc 176756 highsch 9
## 18 32 Private 186824 highsch 9
## 19 38 Private 28887 highsch 7
## 20 43 SelfEmpNotInc 292175 master 14
## 21 40 Private 193524 phd 16
## 22 54 Private 302146 highsch 9
## 23 35 FedGov 76845 highsch 5
## 24 43 Private 117037 highsch 7
## 25 59 Private 109015 highsch 9
## 26 56 LocGov 216851 undergrad 13
## 27 19 Private 168294 highsch 9
## 28 54 unemployed 180211 assoc 10
## 29 39 Private 367260 highsch 9
## 30 49 Private 193366 highsch 9
## 31 23 LocGov 190709 assoc 12
## 32 20 Private 266015 assoc 10
## 33 45 Private 386940 undergrad 13
## 34 30 FedGov 59951 assoc 10
## 35 22 StateGov 311512 assoc 10
## 36 48 Private 242406 highsch 7
## 37 21 Private 197200 assoc 10
## 38 19 Private 544091 highsch 9
## 39 31 Private 84154 assoc 10
## 40 48 SelfEmpNotInc 265477 assoc 12
## 41 31 Private 507875 highsch 5
## 42 53 SelfEmpNotInc 88506 undergrad 13
## 43 24 Private 172987 undergrad 13
## 44 49 Private 94638 highsch 9
## 45 25 Private 289980 highsch 9
## 46 57 FedGov 337895 undergrad 13
## 47 53 Private 144361 highsch 9
## 48 44 Private 128354 master 14
## 49 41 StateGov 101603 assoc 11
## 50 29 Private 271466 assoc 11
## 51 25 Private 32275 assoc 10
## 52 18 Private 226956 highsch 9
## 53 47 Private 51835 undergrad 15
## 54 50 FedGov 251585 undergrad 13
## 55 47 SelfEmpInc 109832 highsch 9
## 56 43 Private 237993 assoc 10
## 57 46 Private 216666 primary 3
## 58 35 Private 56352 assoc 11
## 59 41 Private 147372 highsch 9
## 60 30 Private 188146 highsch 9
## 61 30 Private 59496 undergrad 13
## 62 32 unemployed 293936 secondary 4
## 63 48 Private 149640 highsch 9
## 64 42 Private 116632 phd 16
## 65 29 Private 105598 assoc 10
## 66 36 Private 155537 highsch 9
## 67 28 Private 183175 assoc 10
## 68 53 Private 169846 highsch 9
## 69 49 SelfEmpInc 191681 assoc 10
## 70 25 unemployed 200681 assoc 10
## 71 19 Private 101509 assoc 10
## 72 31 Private 309974 undergrad 13
## 73 29 SelfEmpNotInc 162298 undergrad 13
## 74 23 Private 211678 assoc 10
## 75 79 Private 124744 assoc 10
## 76 27 Private 213921 highsch 9
## 77 40 Private 32214 assoc 12
## 78 67 unemployed 212759 highsch 6
## 79 18 Private 309634 highsch 7
## 80 31 LocGov 125927 secondary 4
## 81 18 Private 446839 highsch 9
## 82 52 Private 276515 undergrad 13
## 83 46 Private 51618 highsch 9
## 84 59 Private 159937 highsch 9
## 85 44 Private 343591 highsch 9
## 86 53 Private 346253 highsch 9
## 87 49 LocGov 268234 highsch 9
## 88 33 Private 202051 master 14
## 89 30 Private 54334 highsch 5
## 90 43 FedGov 410867 phd 16
## 91 57 Private 249977 assoc 11
## 92 37 Private 286730 assoc 10
## 93 28 Private 212563 assoc 10
## 94 30 Private 117747 highsch 9
## 95 34 LocGov 226296 undergrad 13
## 96 29 LocGov 115585 assoc 10
## 97 48 SelfEmpNotInc 191277 phd 16
## 98 37 Private 202683 assoc 10
## 99 48 Private 171095 assoc 12
## 100 32 FedGov 249409 highsch 9
## 101 76 Private 124191 master 14
## 102 44 Private 198282 undergrad 13
## 103 47 SelfEmpNotInc 149116 master 14
## 104 20 Private 188300 assoc 10
## 105 29 Private 103432 highsch 9
## 106 32 SelfEmpInc 317660 highsch 9
## 107 17 unemployed 304873 highsch 6
## 108 30 Private 194901 highsch 7
## 109 31 LocGov 189265 highsch 9
## 110 42 Private 124692 highsch 9
## 111 24 Private 432376 undergrad 13
## 112 38 Private 65324 undergrad 15
## 113 56 SelfEmpNotInc 335605 highsch 9
## 114 28 Private 377869 assoc 10
## 115 36 Private 102864 highsch 9
## 116 53 Private 95647 highsch 5
## 117 56 SelfEmpInc 303090 assoc 10
## 118 49 LocGov 197371 assoc 11
## 119 55 Private 247552 assoc 10
## 120 22 Private 102632 highsch 9
## 121 21 Private 199915 assoc 10
## 122 40 Private 118853 undergrad 13
## 123 30 Private 77143 undergrad 13
## 124 29 StateGov 267989 undergrad 13
## 125 19 Private 301606 assoc 10
## 126 47 Private 287828 undergrad 13
## 127 20 Private 111697 assoc 10
## 128 31 Private 114937 assoc 12
## 129 35 unemployed 129305 highsch 9
## 130 39 Private 365739 assoc 10
## 131 28 Private 69621 assoc 12
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## 5874 30 SelfEmpNotInc 45796 assoc 10
## 5875 59 Private 108496 assoc 10
## 5876 41 SelfEmpNotInc 120539 assoc 10
## 5877 36 SelfEmpNotInc 164526 master 14
## 5878 37 Private 323155 primary 2
## 5879 28 Private 65389 highsch 9
## 5880 19 Private 414871 highsch 9
## 5881 28 Private 161607 assoc 10
## 5882 62 Private 224953 highsch 6
## marital_status occupation household_role race gender
## 1 notmarried clerical tenant white male
## 2 married highskill husband white male
## 3 divorce lowskill tenant white male
## 4 married lowskill husband black male
## 5 married highskill wife black female
## 6 married highskill wife white female
## 7 married lowskill tenant black female
## 8 married highskill husband white male
## 9 notmarried highskill tenant white female
## 10 married highskill husband white male
## 11 married highskill husband black male
## 12 married highskill husband Asian_Pac_Islander male
## 13 notmarried clerical depchild white female
## 14 notmarried highskill tenant black male
## 15 married midskill husband Asian_Pac_Islander male
## 16 married midskill husband native male
## 17 notmarried agriculture depchild white male
## 18 notmarried midskill single_occ white male
## 19 married highskill husband white male
## 20 divorce highskill single_occ white female
## 21 married highskill husband white male
## 22 divorce lowskill single_occ black female
## 23 married agriculture husband black male
## 24 married midskill husband white male
## 25 divorce highskill single_occ white female
## 26 married highskill husband white male
## 27 notmarried midskill depchild white male
## 28 married other husband Asian_Pac_Islander male
## 29 divorce highskill tenant white male
## 30 married midskill husband white male
## 31 notmarried highskill tenant white male
## 32 notmarried highskill depchild black male
## 33 divorce highskill depchild white male
## 34 married clerical depchild white male
## 35 married lowskill husband black male
## 36 notmarried midskill single_occ white male
## 37 notmarried midskill depchild white male
## 38 married clerical wife white female
## 39 married highskill husband white male
## 40 married highskill husband white male
## 41 married midskill husband white male
## 42 married highskill husband white male
## 43 married highskill husband white male
## 44 divorce clerical single_occ white female
## 45 notmarried lowskill tenant white male
## 46 married highskill husband black male
## 47 married midskill husband white male
## 48 divorce highskill single_occ white female
## 49 married midskill husband white male
## 50 notmarried highskill tenant white male
## 51 married highskill wife other female
## 52 notmarried lowskill depchild white female
## 53 married highskill wife white female
## 54 divorce highskill tenant white male
## 55 divorce highskill tenant white male
## 56 married highskill husband white male
## 57 married midskill husband white male
## 58 married lowskill husband white male
## 59 married clerical husband white male
## 60 married midskill husband white male
## 61 married highskill husband white male
## 62 married other tenant white male
## 63 married midskill husband white male
## 64 married highskill husband white male
## 65 divorce highskill tenant white male
## 66 married midskill husband white male
## 67 divorce clerical tenant white female
## 68 married clerical wife white female
## 69 married highskill husband white male
## 70 notmarried other depchild white male
## 71 notmarried highskill depchild white male
## 72 divorce highskill depchild black female
## 73 married highskill husband white male
## 74 notmarried midskill tenant white male
## 75 married highskill relative white male
## 76 notmarried lowskill depchild white male
## 77 married clerical husband white male
## 78 married other husband white male
## 79 notmarried lowskill depchild white female
## 80 married agriculture husband white male
## 81 notmarried highskill tenant white male
## 82 married lowskill husband white male
## 83 married lowskill wife white female
## 84 married highskill husband white male
## 85 divorce midskill tenant white female
## 86 divorce highskill depchild white female
## 87 married highskill husband white male
## 88 married highskill husband white male
## 89 notmarried highskill tenant white male
## 90 notmarried highskill tenant white female
## 91 married highskill husband white male
## 92 divorce midskill single_occ white female
## 93 divorce midskill single_occ black female
## 94 married highskill wife Asian_Pac_Islander female
## 95 married highskill husband white male
## 96 notmarried lowskill tenant white male
## 97 married highskill husband white male
## 98 married highskill husband white male
## 99 divorce highskill single_occ white female
## 100 notmarried lowskill depchild black male
## 101 married highskill husband white male
## 102 married highskill husband white male
## 103 notmarried highskill tenant white female
## 104 notmarried highskill depchild white female
## 105 notmarried midskill tenant white male
## 106 married midskill husband white male
## 107 notmarried other depchild white female
## 108 notmarried lowskill depchild white male
## 109 notmarried clerical tenant white female
## 110 married lowskill husband white male
## 111 notmarried highskill relative white male
## 112 married highskill husband white male
## 113 married lowskill husband white male
## 114 married highskill wife white female
## 115 notmarried midskill depchild white female
## 116 married lowskill husband white male
## 117 married highskill husband white male
## 118 married midskill husband black male
## 119 married highskill husband white male
## 120 notmarried midskill tenant white male
## 121 notmarried lowskill depchild white female
## 122 married highskill husband white male
## 123 notmarried highskill depchild black male
## 124 married highskill husband white male
## 125 notmarried lowskill depchild black male
## 126 married highskill wife white female
## 127 notmarried clerical depchild white female
## 128 married clerical husband white male
## 129 married other husband white male
## 130 divorce midskill tenant white male
## 131 notmarried highskill tenant white female
## 132 notmarried lowskill tenant white female
## 133 married midskill husband white male
## 134 married highskill husband white male
## 135 divorce highskill tenant black female
## 136 married highskill husband white male
## 137 married midskill husband white male
## 138 married clerical wife white female
## 139 notmarried highskill depchild white male
## 140 married midskill husband Asian_Pac_Islander male
## 141 married midskill husband white male
## 142 notmarried highskill depchild white female
## 143 notmarried lowskill depchild white male
## 144 notmarried highskill tenant white female
## 145 notmarried highskill relative white male
## 146 notmarried midskill single_occ black female
## 147 married lowskill husband white male
## 148 widowed lowskill single_occ white female
## 149 married midskill husband white male
## 150 divorce other tenant white female
## 151 divorce clerical single_occ white female
## 152 notmarried clerical tenant white female
## 153 notmarried highskill relative Asian_Pac_Islander female
## 154 notmarried midskill depchild white male
## 155 divorce other tenant white male
## 156 married midskill husband white male
## 157 notmarried lowskill tenant white male
## 158 divorce highskill single_occ black male
## 159 divorce midskill tenant white male
## 160 divorce lowskill relative black male
## 161 divorce other tenant white female
## 162 notmarried lowskill single_occ white female
## 163 divorce highskill single_occ Asian_Pac_Islander female
## 164 notmarried highskill tenant Asian_Pac_Islander female
## 165 married highskill husband white male
## 166 divorce lowskill single_occ white female
## 167 notmarried highskill tenant white male
## 168 widowed highskill single_occ white male
## 169 notmarried lowskill depchild white male
## 170 widowed highskill tenant white female
## 171 notmarried lowskill tenant white male
## 172 married highskill husband white male
## 173 married highskill husband white male
## 174 notmarried midskill tenant white male
## 175 married highskill husband white male
## 176 married highskill single_occ white female
## 177 notmarried midskill depchild black male
## 178 notmarried lowskill tenant black male
## 179 notmarried lowskill depchild white male
## 180 married highskill husband white male
## 181 married highskill husband black male
## 182 divorce clerical tenant white female
## 183 notmarried midskill tenant black male
## 184 married midskill husband white male
## 185 married clerical husband white male
## 186 married clerical husband black male
## 187 divorce highskill tenant white female
## 188 divorce other tenant white female
## 189 married highskill wife white female
## 190 married highskill husband white male
## 191 divorce midskill tenant black female
## 192 married midskill husband white male
## 193 married midskill husband black male
## 194 married highskill husband white male
## 195 married highskill husband white male
## 196 married midskill relative white male
## 197 divorce midskill tenant white male
## 198 notmarried highskill tenant white female
## 199 married highskill relative white male
## 200 notmarried highskill tenant white male
## 201 notmarried highskill tenant white female
## 202 notmarried other depchild white male
## 203 married highskill husband white male
## 204 notmarried highskill tenant white female
## 205 divorce clerical single_occ white female
## 206 notmarried highskill depchild white female
## 207 married lowskill husband black male
## 208 divorce midskill tenant white male
## 209 married highskill wife white female
## 210 notmarried highskill depchild white female
## 211 married midskill husband black male
## 212 married midskill husband white male
## 213 notmarried highskill tenant native female
## 214 married midskill husband Asian_Pac_Islander male
## 215 married midskill husband white male
## 216 notmarried highskill tenant black male
## 217 divorce highskill tenant white female
## 218 married highskill husband white male
## 219 married highskill husband white male
## 220 notmarried midskill tenant white male
## 221 married lowskill husband white male
## 222 divorce other tenant white male
## 223 notmarried lowskill tenant black male
## 224 married clerical husband black male
## 225 notmarried midskill tenant white female
## 226 notmarried highskill depchild white female
## 227 divorce other tenant native female
## 228 married agriculture husband white male
## 229 widowed clerical tenant white female
## 230 divorce clerical tenant white female
## 231 divorce midskill tenant black male
## 232 divorce highskill single_occ black female
## 233 notmarried clerical tenant white male
## 234 divorce midskill relative other female
## 235 notmarried highskill tenant white male
## 236 married lowskill husband white male
## 237 notmarried clerical tenant white female
## 238 married highskill husband white male
## 239 married highskill husband white male
## 240 notmarried midskill depchild white male
## 241 married highskill husband white male
## 242 married lowskill wife white female
## 243 married highskill husband white male
## 244 notmarried other depchild white male
## 245 divorce midskill tenant white male
## 246 married clerical husband white male
## 247 married clerical husband white male
## 248 notmarried lowskill tenant white female
## 249 married highskill husband white male
## 250 married clerical husband white male
## 251 married highskill wife white female
## 252 married clerical wife black female
## 253 widowed lowskill single_occ black female
## 254 notmarried clerical depchild white male
## 255 notmarried midskill tenant white male
## 256 married agriculture husband Asian_Pac_Islander male
## 257 notmarried lowskill depchild white female
## 258 divorce lowskill tenant white female
## 259 widowed highskill tenant white female
## 260 divorce midskill tenant white male
## 261 divorce highskill depchild white female
## 262 married highskill husband white male
## 263 notmarried lowskill depchild white male
## 264 married highskill husband white male
## 265 married midskill husband white male
## 266 married highskill husband Asian_Pac_Islander male
## 267 notmarried other depchild white male
## 268 married highskill husband white male
## 269 notmarried highskill tenant white female
## 270 married midskill wife white female
## 271 married highskill husband white male
## 272 notmarried lowskill depchild white male
## 273 married agriculture husband white male
## 274 notmarried highskill depchild white male
## 275 divorce highskill depchild white female
## 276 married lowskill husband black male
## 277 married midskill husband white male
## 278 notmarried highskill tenant white female
## 279 notmarried highskill depchild white female
## 280 married midskill wife white female
## 281 notmarried highskill tenant white female
## 282 married midskill husband white male
## 283 divorce highskill single_occ black female
## 284 divorce highskill tenant white female
## 285 notmarried lowskill depchild white male
## 286 married highskill husband white male
## 287 married highskill husband white male
## 288 divorce lowskill tenant white male
## 289 notmarried highskill tenant white female
## 290 married midskill husband white male
## 291 married highskill husband white male
## 292 divorce highskill tenant white female
## 293 notmarried highskill tenant white male
## 294 notmarried highskill depchild white male
## 295 notmarried highskill single_occ black female
## 296 married midskill husband white male
## 297 divorce highskill tenant white female
## 298 married other wife Asian_Pac_Islander female
## 299 notmarried midskill depchild white male
## 300 divorce highskill tenant white male
## 301 married clerical husband white male
## 302 notmarried lowskill tenant Asian_Pac_Islander female
## 303 married midskill husband white male
## 304 married highskill husband white male
## 305 married clerical wife Asian_Pac_Islander female
## 306 divorce clerical tenant white female
## 307 notmarried highskill tenant white male
## 308 notmarried highskill tenant white female
## 309 married clerical husband white male
## 310 married agriculture husband white male
## 311 notmarried highskill tenant white male
## 312 married highskill husband Asian_Pac_Islander male
## 313 notmarried other depchild black female
## 314 divorce highskill tenant black female
## 315 notmarried highskill depchild white male
## 316 notmarried midskill tenant white male
## 317 married highskill husband white male
## 318 married midskill relative white female
## 319 notmarried highskill tenant white female
## 320 widowed lowskill tenant white female
## 321 married highskill husband white male
## 322 married midskill husband white male
## 323 divorce midskill tenant white male
## 324 notmarried highskill tenant white female
## 325 married midskill husband white male
## 326 married agriculture husband white male
## 327 notmarried other depchild black female
## 328 married highskill wife white female
## 329 married lowskill husband white male
## 330 notmarried midskill tenant white male
## 331 notmarried midskill tenant black male
## 332 notmarried clerical tenant white male
## 333 notmarried lowskill tenant white male
## 334 married clerical husband white male
## 335 married midskill husband white male
## 336 notmarried lowskill relative white male
## 337 notmarried midskill relative white male
## 338 notmarried highskill tenant white male
## 339 notmarried highskill single_occ black male
## 340 married lowskill husband black male
## 341 notmarried highskill tenant white male
## 342 married midskill husband white male
## 343 married highskill husband white male
## 344 notmarried agriculture tenant white male
## 345 notmarried highskill depchild black female
## 346 notmarried highskill tenant white male
## 347 married other husband white male
## 348 divorce other single_occ white female
## 349 married lowskill tenant white male
## 350 notmarried highskill depchild white male
## 351 married highskill husband white male
## 352 married lowskill husband white male
## 353 married highskill husband white male
## 354 married midskill husband white male
## 355 married other husband white male
## 356 married clerical husband white male
## 357 widowed lowskill single_occ other male
## 358 married highskill husband white male
## 359 married clerical wife white female
## 360 married midskill husband white male
## 361 married lowskill wife white female
## 362 married highskill husband white male
## 363 married midskill husband white male
## 364 divorce highskill tenant white male
## 365 married highskill tenant white male
## 366 married midskill husband white male
## 367 married highskill husband white male
## 368 notmarried highskill tenant white female
## 369 divorce highskill tenant white female
## 370 divorce highskill single_occ white female
## 371 notmarried clerical single_occ white female
## 372 notmarried lowskill depchild white male
## 373 married midskill husband white male
## 374 married midskill husband black male
## 375 notmarried midskill tenant black male
## 376 notmarried clerical depchild white female
## 377 married midskill wife white female
## 378 notmarried highskill tenant white male
## 379 divorce highskill tenant white male
## 380 married highskill husband white male
## 381 divorce midskill tenant white male
## 382 divorce midskill tenant white female
## 383 married midskill husband white male
## 384 divorce highskill tenant white female
## 385 married midskill wife white female
## 386 notmarried lowskill tenant white female
## 387 divorce midskill tenant white male
## 388 married clerical wife white female
## 389 notmarried clerical depchild white female
## 390 notmarried lowskill relative white female
## 391 married lowskill wife white female
## 392 notmarried midskill tenant white male
## 393 divorce midskill tenant white male
## 394 married highskill husband white male
## 395 notmarried lowskill tenant white female
## 396 married highskill husband white male
## 397 divorce lowskill single_occ white female
## 398 notmarried other relative white female
## 399 married highskill husband white male
## 400 married highskill husband white male
## 401 notmarried lowskill depchild white male
## 402 notmarried lowskill tenant white female
## 403 married highskill husband white male
## 404 divorce highskill tenant white female
## 405 married lowskill husband other male
## 406 married highskill husband white male
## 407 married clerical husband white male
## 408 married midskill husband white male
## 409 married other wife white female
## 410 married highskill husband white male
## 411 divorce lowskill tenant black female
## 412 notmarried lowskill single_occ black female
## 413 divorce highskill single_occ white female
## 414 married highskill husband white male
## 415 married midskill husband white male
## 416 divorce midskill tenant white male
## 417 married midskill husband white male
## 418 notmarried lowskill depchild white female
## 419 divorce midskill tenant black male
## 420 divorce midskill single_occ white female
## 421 married highskill husband white male
## 422 notmarried lowskill depchild white male
## 423 married highskill wife white female
## 424 divorce agriculture tenant white male
## 425 notmarried midskill single_occ white female
## 426 married midskill husband white male
## 427 notmarried highskill tenant white male
## 428 notmarried lowskill tenant white male
## 429 married highskill husband white male
## 430 married highskill husband white male
## 431 widowed other tenant white male
## 432 notmarried other depchild white male
## 433 married highskill tenant white female
## 434 married lowskill wife white female
## 435 notmarried midskill depchild Asian_Pac_Islander male
## 436 married highskill husband white male
## 437 notmarried agriculture tenant black male
## 438 notmarried highskill tenant white female
## 439 married clerical husband white male
## 440 married clerical wife white female
## 441 notmarried highskill tenant white male
## 442 divorce clerical tenant white female
## 443 notmarried lowskill depchild white male
## 444 married highskill husband white male
## 445 notmarried midskill relative white male
## 446 notmarried highskill tenant white male
## 447 widowed highskill single_occ black female
## 448 married midskill husband native male
## 449 married midskill husband white male
## 450 notmarried other depchild white female
## 451 notmarried midskill depchild black female
## 452 married midskill husband white male
## 453 married midskill husband white male
## 454 married highskill husband black male
## 455 married highskill wife white female
## 456 married midskill husband Asian_Pac_Islander male
## 457 married highskill wife white female
## 458 married midskill husband black male
## 459 married midskill husband white male
## 460 married other husband white male
## 461 notmarried agriculture depchild white male
## 462 married midskill husband black male
## 463 married highskill husband white male
## 464 married midskill single_occ white male
## 465 notmarried highskill tenant white female
## 466 married clerical wife white female
## 467 divorce midskill single_occ white male
## 468 married midskill husband white male
## 469 married highskill husband white male
## 470 married highskill wife white female
## 471 notmarried highskill single_occ white male
## 472 notmarried other depchild white male
## 473 notmarried midskill tenant white male
## 474 notmarried midskill tenant white male
## 475 divorce lowskill tenant white female
## 476 notmarried midskill tenant white female
## 477 notmarried lowskill tenant white male
## 478 notmarried lowskill depchild white male
## 479 notmarried lowskill tenant white male
## 480 notmarried lowskill single_occ white female
## 481 married midskill husband white male
## 482 married midskill husband white male
## 483 notmarried highskill tenant white male
## 484 married midskill single_occ white female
## 485 notmarried other relative black male
## 486 married clerical depchild black female
## 487 married other husband white male
## 488 married midskill husband white male
## 489 divorce highskill single_occ white female
## 490 married highskill husband white male
## 491 notmarried lowskill tenant white female
## 492 divorce clerical tenant white male
## 493 notmarried highskill tenant white female
## 494 divorce highskill single_occ white female
## 495 notmarried midskill depchild white male
## 496 divorce clerical single_occ white female
## 497 notmarried midskill depchild white male
## 498 notmarried lowskill depchild white male
## 499 notmarried highskill tenant white female
## 500 married other husband white male
## 501 notmarried agriculture tenant white male
## 502 married midskill husband white male
## 503 married clerical husband white male
## 504 divorce lowskill single_occ white female
## 505 notmarried highskill tenant white female
## 506 notmarried highskill tenant black male
## 507 notmarried highskill depchild white male
## 508 married highskill husband white male
## 509 divorce clerical single_occ black female
## 510 divorce lowskill single_occ white female
## 511 married lowskill single_occ white male
## 512 widowed other single_occ black female
## 513 notmarried midskill tenant white male
## 514 married highskill husband white male
## 515 divorce midskill tenant white male
## 516 widowed other single_occ white female
## 517 notmarried clerical tenant native female
## 518 notmarried other depchild white male
## 519 notmarried other single_occ white female
## 520 married highskill husband white male
## 521 married highskill husband white male
## 522 married highskill husband white male
## 523 divorce lowskill tenant black male
## 524 married highskill husband white male
## 525 divorce midskill single_occ white male
## 526 married highskill wife white female
## 527 married highskill husband white male
## 528 divorce clerical tenant white female
## 529 married midskill husband white male
## 530 married highskill husband white male
## 531 divorce midskill single_occ other female
## 532 notmarried midskill tenant white male
## 533 married lowskill husband black male
## 534 married highskill wife white female
## 535 married highskill husband white male
## 536 married highskill husband white male
## 537 widowed lowskill tenant black female
## 538 divorce midskill tenant white male
## 539 notmarried agriculture tenant white female
## 540 notmarried other single_occ black female
## 541 married midskill husband white male
## 542 notmarried highskill depchild white male
## 543 notmarried lowskill single_occ white female
## 544 notmarried midskill tenant white male
## 545 married highskill husband white male
## 546 notmarried lowskill tenant white female
## 547 married lowskill wife white female
## 548 married highskill husband white male
## 549 notmarried lowskill depchild white male
## 550 notmarried midskill single_occ white male
## 551 divorce highskill tenant white male
## 552 divorce lowskill single_occ white female
## 553 widowed clerical single_occ white female
## 554 married midskill wife black female
## 555 divorce midskill single_occ black female
## 556 notmarried midskill tenant white female
## 557 married midskill husband white male
## 558 divorce clerical single_occ white female
## 559 married highskill wife white female
## 560 married lowskill husband white male
## 561 notmarried midskill tenant white male
## 562 married highskill husband white male
## 563 married highskill husband white male
## 564 notmarried lowskill single_occ black female
## 565 married highskill husband white male
## 566 notmarried lowskill depchild native male
## 567 married highskill husband white male
## 568 divorce lowskill single_occ white female
## 569 divorce midskill tenant white male
## 570 divorce highskill single_occ white male
## 571 notmarried midskill depchild white male
## 572 married highskill husband white male
## 573 notmarried highskill depchild white male
## 574 married midskill husband white male
## 575 notmarried highskill tenant white female
## 576 married highskill wife white male
## 577 notmarried other depchild white male
## 578 divorce highskill single_occ white female
## 579 notmarried highskill tenant white female
## 580 married midskill husband white male
## 581 married other husband white male
## 582 married highskill husband white male
## 583 married midskill husband white male
## 584 married midskill husband white male
## 585 notmarried highskill tenant white male
## 586 notmarried highskill depchild white female
## 587 notmarried clerical tenant white male
## 588 notmarried lowskill depchild white male
## 589 married midskill husband white male
## 590 divorce highskill tenant white male
## 591 married other husband white male
## 592 married other wife Asian_Pac_Islander female
## 593 notmarried midskill tenant white male
## 594 married highskill husband white male
## 595 notmarried clerical depchild white female
## 596 notmarried highskill depchild white male
## 597 notmarried other single_occ native female
## 598 married highskill husband white male
## 599 notmarried highskill tenant white male
## 600 notmarried highskill tenant white female
## 601 notmarried highskill tenant white male
## 602 notmarried clerical depchild white male
## 603 divorce lowskill tenant white female
## 604 married lowskill husband white male
## 605 notmarried agriculture tenant white male
## 606 notmarried clerical single_occ black female
## 607 married midskill husband white male
## 608 notmarried midskill tenant native male
## 609 divorce highskill tenant black male
## 610 notmarried lowskill depchild white female
## 611 divorce highskill tenant white female
## 612 notmarried midskill tenant white male
## 613 married lowskill husband white male
## 614 notmarried highskill depchild white male
## 615 notmarried lowskill depchild white female
## 616 notmarried lowskill depchild white male
## 617 married midskill husband black male
## 618 married highskill husband white male
## 619 married highskill husband white male
## 620 married highskill husband white male
## 621 notmarried highskill depchild white female
## 622 widowed lowskill tenant black female
## 623 widowed lowskill single_occ black female
## 624 married midskill husband white male
## 625 married lowskill single_occ white male
## 626 notmarried agriculture depchild black male
## 627 married highskill husband white male
## 628 married highskill husband white male
## 629 married highskill husband white male
## 630 married highskill husband white male
## 631 notmarried lowskill single_occ black female
## 632 divorce lowskill single_occ black female
## 633 married midskill husband white male
## 634 married clerical wife black female
## 635 married midskill husband white male
## 636 notmarried lowskill depchild white female
## 637 notmarried lowskill tenant white male
## 638 married highskill husband white male
## 639 married midskill husband white male
## 640 married highskill husband white male
## 641 divorce midskill tenant white male
## 642 married midskill husband white male
## 643 notmarried lowskill depchild white male
## 644 married highskill husband white male
## 645 married midskill husband white male
## 646 divorce highskill tenant white male
## 647 divorce clerical single_occ white female
## 648 married midskill husband white male
## 649 married other husband white male
## 650 married highskill husband white male
## 651 notmarried lowskill depchild white male
## 652 notmarried highskill relative white male
## 653 married highskill husband Asian_Pac_Islander male
## 654 divorce highskill tenant white female
## 655 notmarried midskill depchild white female
## 656 notmarried clerical single_occ Asian_Pac_Islander female
## 657 widowed other single_occ white female
## 658 married midskill husband white male
## 659 married midskill husband white male
## 660 married midskill husband white male
## 661 married lowskill husband black male
## 662 notmarried highskill tenant white male
## 663 notmarried highskill tenant white male
## 664 notmarried highskill tenant white male
## 665 notmarried highskill tenant white female
## 666 divorce highskill single_occ white female
## 667 married midskill husband white male
## 668 married midskill husband black male
## 669 married other depchild Asian_Pac_Islander female
## 670 notmarried midskill tenant white male
## 671 divorce highskill tenant white male
## 672 notmarried other depchild white female
## 673 notmarried agriculture depchild white male
## 674 married highskill husband white male
## 675 married highskill husband white male
## 676 notmarried highskill depchild white female
## 677 notmarried lowskill depchild white male
## 678 married clerical husband white male
## 679 notmarried midskill tenant white male
## 680 widowed highskill tenant white male
## 681 notmarried highskill depchild white male
## 682 married midskill husband white male
## 683 married midskill husband white male
## 684 divorce highskill single_occ black male
## 685 notmarried clerical depchild white female
## 686 divorce highskill tenant white female
## 687 notmarried other tenant white male
## 688 married highskill wife white female
## 689 notmarried highskill tenant white male
## 690 notmarried lowskill relative white male
## 691 married agriculture husband white male
## 692 notmarried midskill depchild black female
## 693 married highskill husband white male
## 694 notmarried highskill depchild white male
## 695 notmarried highskill tenant white female
## 696 notmarried other relative white male
## 697 notmarried highskill tenant white male
## 698 married highskill husband white male
## 699 notmarried highskill depchild white female
## 700 notmarried highskill depchild white female
## 701 notmarried highskill tenant white male
## 702 widowed highskill single_occ white female
## 703 notmarried lowskill depchild white male
## 704 married midskill husband white male
## 705 divorce highskill single_occ white female
## 706 divorce highskill depchild white female
## 707 married lowskill husband black male
## 708 notmarried highskill tenant black male
## 709 married midskill husband white male
## 710 notmarried lowskill relative white male
## 711 married highskill husband white male
## 712 divorce clerical single_occ black female
## 713 divorce highskill tenant white male
## 714 married highskill husband white male
## 715 divorce clerical single_occ white female
## 716 divorce lowskill single_occ black female
## 717 married midskill husband white male
## 718 notmarried highskill depchild white female
## 719 notmarried highskill depchild white female
## 720 married highskill husband white male
## 721 notmarried lowskill tenant white male
## 722 married midskill husband white male
## 723 notmarried highskill tenant white male
## 724 married highskill husband white male
## 725 married highskill husband white male
## 726 notmarried highskill tenant white male
## 727 married highskill wife white female
## 728 notmarried lowskill tenant white female
## 729 married lowskill husband white male
## 730 married highskill husband white male
## 731 notmarried lowskill tenant white female
## 732 divorce clerical tenant Asian_Pac_Islander female
## 733 married clerical husband white male
## 734 notmarried lowskill depchild white female
## 735 married other wife black female
## 736 married midskill husband white male
## 737 notmarried highskill tenant black female
## 738 married highskill husband white male
## 739 widowed lowskill single_occ black female
## 740 married highskill husband white male
## 741 notmarried lowskill relative white female
## 742 married midskill husband white male
## 743 notmarried highskill tenant white female
## 744 notmarried highskill depchild white male
## 745 notmarried lowskill depchild white male
## 746 married highskill wife white female
## 747 married midskill husband white male
## 748 notmarried clerical depchild black female
## 749 notmarried midskill depchild black male
## 750 married highskill husband white male
## 751 married highskill husband white male
## 752 notmarried highskill single_occ white male
## 753 married midskill husband white male
## 754 notmarried midskill single_occ white male
## 755 notmarried lowskill tenant white male
## 756 notmarried midskill depchild white male
## 757 married midskill husband white male
## 758 notmarried highskill tenant white female
## 759 married clerical wife white female
## 760 notmarried agriculture tenant black male
## 761 notmarried lowskill tenant white female
## 762 married clerical wife white female
## 763 divorce highskill single_occ white male
## 764 married highskill husband white male
## 765 notmarried highskill tenant white male
## 766 divorce lowskill single_occ white female
## 767 married lowskill relative white male
## 768 married highskill wife white female
## 769 divorce highskill tenant white female
## 770 notmarried midskill tenant white male
## 771 notmarried agriculture tenant white female
## 772 divorce agriculture tenant white male
## 773 divorce midskill single_occ white male
## 774 married highskill wife white female
## 775 notmarried highskill tenant white male
## 776 divorce highskill tenant white male
## 777 divorce clerical single_occ white female
## 778 married highskill husband white male
## 779 married highskill husband white male
## 780 notmarried highskill tenant white male
## 781 married midskill husband white male
## 782 notmarried midskill tenant white female
## 783 married highskill husband white male
## 784 married highskill husband white male
## 785 notmarried highskill tenant other female
## 786 divorce clerical tenant black female
## 787 married agriculture husband white male
## 788 notmarried midskill depchild white male
## 789 married other husband white male
## 790 notmarried other tenant white female
## 791 married highskill husband white male
## 792 married highskill husband white male
## 793 notmarried other depchild white male
## 794 notmarried highskill tenant white female
## 795 married midskill husband white male
## 796 notmarried lowskill relative black male
## 797 married midskill husband white male
## 798 notmarried midskill tenant black male
## 799 notmarried highskill tenant white male
## 800 married highskill husband white male
## 801 divorce midskill tenant white male
## 802 notmarried lowskill depchild black male
## 803 notmarried agriculture single_occ white male
## 804 divorce clerical single_occ white female
## 805 notmarried highskill depchild white female
## 806 married midskill husband white male
## 807 married other relative white male
## 808 married highskill husband white male
## 809 married highskill husband white male
## 810 notmarried midskill depchild white male
## 811 divorce lowskill depchild white female
## 812 notmarried other depchild black male
## 813 divorce highskill tenant white male
## 814 notmarried clerical depchild white male
## 815 notmarried agriculture depchild white male
## 816 married lowskill wife white female
## 817 notmarried highskill tenant white female
## 818 married midskill husband white male
## 819 married midskill husband white male
## 820 married midskill husband white male
## 821 notmarried clerical single_occ white male
## 822 notmarried highskill tenant white female
## 823 notmarried highskill depchild white male
## 824 widowed clerical tenant white female
## 825 married highskill husband white male
## 826 widowed midskill single_occ white male
## 827 widowed lowskill tenant white female
## 828 divorce midskill single_occ white female
## 829 notmarried clerical tenant white male
## 830 notmarried highskill depchild white male
## 831 married other husband white male
## 832 married clerical wife white female
## 833 notmarried lowskill depchild white male
## 834 married highskill husband white male
## 835 widowed other tenant white female
## 836 married midskill husband white male
## 837 divorce clerical tenant white male
## 838 divorce highskill tenant white female
## 839 notmarried highskill tenant white male
## 840 widowed agriculture tenant white male
## 841 notmarried highskill depchild white male
## 842 married midskill husband white male
## 843 married highskill husband white male
## 844 widowed highskill tenant white female
## 845 married midskill husband white male
## 846 divorce highskill tenant white male
## 847 divorce lowskill tenant white female
## 848 married highskill husband white male
## 849 married midskill husband white male
## 850 notmarried midskill depchild white male
## 851 widowed highskill tenant white female
## 852 divorce clerical tenant white female
## 853 married highskill husband white male
## 854 notmarried highskill tenant white female
## 855 divorce highskill depchild white male
## 856 notmarried midskill depchild white male
## 857 notmarried clerical depchild white female
## 858 married lowskill husband white male
## 859 notmarried highskill depchild white female
## 860 notmarried highskill tenant white female
## 861 notmarried clerical depchild white female
## 862 married agriculture husband white male
## 863 married highskill husband white male
## 864 married highskill husband white male
## 865 married midskill husband white male
## 866 notmarried highskill tenant white female
## 867 notmarried lowskill tenant white male
## 868 notmarried highskill depchild white male
## 869 married agriculture husband white male
## 870 divorce highskill depchild white male
## 871 married highskill husband white male
## 872 notmarried midskill depchild white male
## 873 married highskill husband white male
## 874 married highskill husband Asian_Pac_Islander male
## 875 divorce lowskill single_occ black male
## 876 married highskill husband white male
## 877 notmarried lowskill single_occ black female
## 878 divorce clerical single_occ white female
## 879 married midskill husband white male
## 880 notmarried clerical tenant white female
## 881 divorce midskill tenant white female
## 882 married clerical relative white male
## 883 married midskill husband white male
## 884 married highskill husband white male
## 885 married midskill wife white female
## 886 married other husband white male
## 887 notmarried highskill depchild white male
## 888 married midskill husband white male
## 889 married highskill husband black male
## 890 divorce highskill tenant white female
## 891 divorce other single_occ black female
## 892 notmarried highskill tenant white male
## 893 married clerical wife Asian_Pac_Islander female
## 894 married highskill husband white male
## 895 married agriculture husband white male
## 896 married highskill husband white male
## 897 divorce highskill depchild white male
## 898 notmarried highskill depchild white female
## 899 married highskill husband white male
## 900 married highskill husband white male
## 901 married lowskill husband white male
## 902 married midskill husband white male
## 903 notmarried lowskill depchild white female
## 904 married other depchild native male
## 905 notmarried highskill tenant white male
## 906 divorce midskill tenant white female
## 907 divorce midskill tenant white male
## 908 notmarried highskill depchild black male
## 909 divorce lowskill depchild white male
## 910 married highskill husband white male
## 911 notmarried highskill tenant white male
## 912 divorce midskill tenant white male
## 913 married agriculture husband white male
## 914 notmarried midskill tenant white male
## 915 notmarried highskill depchild black male
## 916 married highskill husband white male
## 917 married highskill wife Asian_Pac_Islander female
## 918 married highskill husband Asian_Pac_Islander male
## 919 married highskill husband white male
## 920 notmarried lowskill depchild white male
## 921 notmarried highskill tenant white male
## 922 notmarried midskill single_occ white male
## 923 married midskill wife white female
## 924 married clerical wife black female
## 925 married other husband white male
## 926 married midskill husband white male
## 927 married midskill husband white male
## 928 married highskill husband white male
## 929 notmarried highskill tenant white female
## 930 married lowskill tenant white female
## 931 divorce midskill tenant white male
## 932 notmarried other depchild white female
## 933 married lowskill husband white male
## 934 married clerical wife white female
## 935 married midskill husband white male
## 936 married midskill husband white male
## 937 notmarried highskill tenant white female
## 938 married highskill husband white male
## 939 married midskill husband white male
## 940 notmarried highskill tenant white male
## 941 divorce lowskill single_occ native female
## 942 married midskill husband black male
## 943 married highskill husband white male
## 944 notmarried lowskill tenant white female
## 945 married midskill husband white male
## 946 divorce highskill single_occ black female
## 947 divorce clerical single_occ white female
## 948 married highskill wife white female
## 949 divorce midskill single_occ white male
## 950 divorce agriculture single_occ white male
## 951 married highskill wife white female
## 952 married highskill husband white male
## 953 widowed lowskill single_occ black female
## 954 notmarried lowskill depchild white female
## 955 married highskill husband white male
## 956 married midskill husband white male
## 957 married highskill husband white male
## 958 divorce midskill tenant white male
## 959 married highskill husband white male
## 960 married highskill husband white male
## 961 married highskill husband white male
## 962 divorce midskill tenant white female
## 963 married midskill husband white male
## 964 divorce highskill single_occ white female
## 965 married midskill husband white male
## 966 notmarried lowskill depchild white female
## 967 married highskill husband white male
## 968 married midskill husband white male
## 969 married highskill husband Asian_Pac_Islander male
## 970 widowed other tenant white female
## 971 married midskill husband white male
## 972 notmarried highskill tenant white female
## 973 widowed highskill tenant white male
## 974 married midskill husband white male
## 975 notmarried midskill single_occ white male
## 976 notmarried highskill tenant white female
## 977 notmarried highskill tenant white female
## 978 married highskill husband white male
## 979 widowed lowskill tenant black female
## 980 divorce lowskill depchild white male
## 981 notmarried clerical tenant white female
## 982 notmarried clerical tenant white female
## 983 notmarried other single_occ black female
## 984 notmarried agriculture single_occ white male
## 985 widowed highskill tenant white male
## 986 divorce lowskill tenant white female
## 987 notmarried midskill depchild black female
## 988 divorce clerical single_occ white female
## 989 divorce midskill single_occ white male
## 990 widowed highskill tenant white female
## 991 notmarried highskill tenant white male
## 992 married midskill husband white male
## 993 married midskill husband white male
## 994 married clerical husband white male
## 995 notmarried highskill tenant white male
## 996 married highskill husband white male
## 997 divorce highskill tenant white male
## 998 divorce highskill single_occ white male
## 999 married midskill husband white male
## 1000 married highskill husband white male
## 1001 notmarried lowskill depchild white male
## 1002 married highskill husband white male
## 1003 married highskill wife white female
## 1004 notmarried highskill depchild white female
## 1005 notmarried lowskill depchild white male
## 1006 married clerical husband white male
## 1007 divorce midskill tenant black male
## 1008 married highskill husband white male
## 1009 married midskill husband white male
## 1010 married highskill husband white male
## 1011 divorce midskill tenant white female
## 1012 notmarried midskill depchild white male
## 1013 married midskill husband white male
## 1014 married highskill husband white male
## 1015 married highskill husband white male
## 1016 married lowskill husband white male
## 1017 married highskill wife white female
## 1018 widowed lowskill single_occ white female
## 1019 divorce midskill tenant white male
## 1020 notmarried other depchild white male
## 1021 notmarried midskill tenant white male
## 1022 married highskill husband white male
## 1023 divorce highskill tenant white female
## 1024 married lowskill husband white male
## 1025 notmarried midskill single_occ black female
## 1026 married lowskill husband white male
## 1027 notmarried highskill tenant black male
## 1028 married highskill husband white male
## 1029 married clerical wife white female
## 1030 married highskill tenant Asian_Pac_Islander male
## 1031 notmarried clerical tenant white female
## 1032 notmarried lowskill depchild white male
## 1033 notmarried highskill tenant white male
## 1034 notmarried lowskill tenant black female
## 1035 widowed other tenant white male
## 1036 notmarried other single_occ white male
## 1037 married highskill husband white male
## 1038 notmarried lowskill tenant white male
## 1039 divorce lowskill single_occ black female
## 1040 married other wife white female
## 1041 notmarried lowskill tenant white female
## 1042 notmarried midskill depchild black female
## 1043 divorce clerical tenant white female
## 1044 married highskill husband white male
## 1045 notmarried highskill tenant white female
## 1046 divorce highskill single_occ white male
## 1047 notmarried other tenant white female
## 1048 married lowskill husband white male
## 1049 notmarried highskill tenant white female
## 1050 married highskill husband white male
## 1051 married midskill husband white male
## 1052 married highskill husband white male
## 1053 married clerical husband white male
## 1054 married highskill husband white male
## 1055 divorce midskill tenant white male
## 1056 married highskill husband white male
## 1057 married midskill husband white male
## 1058 divorce midskill tenant white female
## 1059 married highskill husband white male
## 1060 married midskill husband white male
## 1061 married highskill husband white male
## 1062 married midskill husband white male
## 1063 married lowskill husband white male
## 1064 divorce lowskill single_occ white female
## 1065 married highskill husband white male
## 1066 notmarried lowskill depchild white male
## 1067 married highskill husband white male
## 1068 married midskill husband white male
## 1069 married highskill husband white male
## 1070 divorce highskill tenant white female
## 1071 notmarried highskill depchild white female
## 1072 married midskill husband white male
## 1073 married midskill husband white male
## 1074 married highskill wife white female
## 1075 notmarried clerical depchild white male
## 1076 notmarried midskill depchild white male
## 1077 notmarried highskill tenant black female
## 1078 notmarried highskill depchild Asian_Pac_Islander male
## 1079 married midskill husband white male
## 1080 married highskill wife white female
## 1081 divorce highskill single_occ white female
## 1082 divorce highskill tenant white female
## 1083 married agriculture husband white male
## 1084 married highskill husband white male
## 1085 married clerical husband white male
## 1086 married midskill husband white male
## 1087 married highskill husband white male
## 1088 notmarried highskill tenant white male
## 1089 married highskill husband white male
## 1090 divorce midskill depchild white male
## 1091 married highskill husband white male
## 1092 notmarried highskill tenant white male
## 1093 married highskill tenant Asian_Pac_Islander male
## 1094 divorce midskill tenant white male
## 1095 notmarried clerical depchild white male
## 1096 notmarried highskill depchild Asian_Pac_Islander male
## 1097 married midskill husband white male
## 1098 notmarried other depchild white female
## 1099 married highskill wife white female
## 1100 divorce highskill single_occ white female
## 1101 married other husband white male
## 1102 married midskill husband white male
## 1103 married highskill husband white male
## 1104 married highskill husband black male
## 1105 married highskill wife white female
## 1106 divorce clerical single_occ white female
## 1107 notmarried highskill tenant white male
## 1108 notmarried lowskill depchild white male
## 1109 married midskill husband white male
## 1110 married midskill husband white male
## 1111 married highskill husband white male
## 1112 married highskill wife white female
## 1113 notmarried clerical tenant white male
## 1114 married highskill wife white female
## 1115 divorce lowskill depchild white female
## 1116 married midskill husband white male
## 1117 married midskill husband white male
## 1118 married midskill husband white male
## 1119 married highskill wife white female
## 1120 married highskill husband Asian_Pac_Islander male
## 1121 married highskill husband white male
## 1122 divorce highskill tenant white female
## 1123 married highskill husband white male
## 1124 married highskill husband white male
## 1125 divorce lowskill single_occ black female
## 1126 notmarried clerical depchild white female
## 1127 married midskill husband white male
## 1128 widowed lowskill tenant black male
## 1129 divorce lowskill single_occ white male
## 1130 married highskill husband white male
## 1131 notmarried midskill tenant black female
## 1132 divorce other tenant white female
## 1133 notmarried lowskill single_occ white female
## 1134 notmarried clerical depchild white female
## 1135 divorce other single_occ white male
## 1136 married highskill husband white male
## 1137 married highskill husband white male
## 1138 married clerical husband black male
## 1139 married midskill husband white male
## 1140 married midskill husband white male
## 1141 notmarried lowskill depchild white female
## 1142 notmarried highskill tenant white male
## 1143 widowed highskill single_occ white female
## 1144 notmarried lowskill tenant white male
## 1145 married highskill husband white male
## 1146 married highskill husband white male
## 1147 notmarried highskill tenant white female
## 1148 married midskill husband white male
## 1149 married highskill husband white male
## 1150 married highskill husband white male
## 1151 notmarried lowskill depchild white male
## 1152 divorce clerical single_occ white female
## 1153 notmarried other tenant white male
## 1154 married midskill husband white male
## 1155 married highskill husband white male
## 1156 married agriculture husband white male
## 1157 married highskill husband white male
## 1158 married highskill wife white female
## 1159 notmarried midskill single_occ Asian_Pac_Islander male
## 1160 notmarried lowskill depchild white male
## 1161 married midskill husband white male
## 1162 widowed midskill single_occ white male
## 1163 married highskill husband white male
## 1164 married highskill husband white male
## 1165 married highskill wife white female
## 1166 divorce clerical tenant black female
## 1167 married clerical husband white male
## 1168 notmarried other depchild white female
## 1169 married highskill husband white male
## 1170 divorce midskill tenant white male
## 1171 divorce clerical tenant white female
## 1172 divorce lowskill tenant black female
## 1173 married agriculture tenant white male
## 1174 notmarried lowskill tenant white male
## 1175 married lowskill husband white male
## 1176 married other husband white male
## 1177 notmarried clerical depchild white female
## 1178 married highskill husband white male
## 1179 notmarried clerical single_occ white female
## 1180 notmarried lowskill tenant Asian_Pac_Islander male
## 1181 married other husband white male
## 1182 divorce highskill tenant white female
## 1183 married highskill husband white male
## 1184 married clerical husband white male
## 1185 married midskill husband white male
## 1186 married other husband white male
## 1187 notmarried highskill depchild black female
## 1188 married midskill husband white male
## 1189 notmarried clerical tenant white female
## 1190 notmarried highskill depchild white male
## 1191 divorce lowskill tenant white male
## 1192 married midskill husband white male
## 1193 married midskill husband white male
## 1194 married highskill husband white male
## 1195 notmarried lowskill depchild white female
## 1196 divorce highskill tenant black female
## 1197 married highskill husband white male
## 1198 married highskill husband white male
## 1199 notmarried midskill depchild white female
## 1200 married midskill husband white male
## 1201 married clerical husband black male
## 1202 married highskill husband white male
## 1203 divorce midskill tenant white male
## 1204 notmarried lowskill tenant white male
## 1205 married midskill husband black male
## 1206 married highskill husband white male
## 1207 married highskill husband white male
## 1208 married highskill husband white male
## 1209 notmarried other depchild white male
## 1210 married midskill husband white male
## 1211 married highskill husband white male
## 1212 divorce clerical depchild white male
## 1213 divorce midskill single_occ white female
## 1214 married highskill husband white male
## 1215 notmarried lowskill depchild white male
## 1216 widowed other tenant white female
## 1217 married midskill husband native male
## 1218 divorce other tenant Asian_Pac_Islander female
## 1219 divorce highskill tenant white male
## 1220 married highskill husband white male
## 1221 married midskill husband white male
## 1222 married midskill husband white male
## 1223 married midskill husband white male
## 1224 widowed clerical depchild white female
## 1225 married midskill husband white male
## 1226 notmarried clerical tenant white female
## 1227 married highskill husband white male
## 1228 married agriculture husband white male
## 1229 divorce clerical single_occ white female
## 1230 married highskill husband white male
## 1231 married highskill husband white male
## 1232 divorce clerical single_occ white female
## 1233 notmarried clerical tenant white female
## 1234 married midskill husband white male
## 1235 divorce lowskill tenant black female
## 1236 notmarried highskill tenant white female
## 1237 notmarried highskill tenant white female
## 1238 married highskill relative white female
## 1239 notmarried lowskill depchild white female
## 1240 notmarried clerical depchild white female
## 1241 notmarried highskill tenant white male
## 1242 divorce clerical tenant white female
## 1243 widowed highskill tenant white female
## 1244 married highskill husband white male
## 1245 notmarried lowskill tenant white male
## 1246 widowed lowskill tenant white female
## 1247 married highskill husband white male
## 1248 divorce clerical tenant white male
## 1249 married clerical wife white female
## 1250 notmarried lowskill depchild white female
## 1251 married highskill husband white male
## 1252 married highskill husband white male
## 1253 married clerical husband white male
## 1254 divorce clerical tenant white female
## 1255 notmarried midskill tenant white male
## 1256 married midskill husband Asian_Pac_Islander male
## 1257 notmarried clerical tenant white female
## 1258 married highskill husband white male
## 1259 married highskill husband white male
## 1260 divorce clerical tenant white male
## 1261 married midskill husband white male
## 1262 married midskill husband white male
## 1263 widowed other tenant white female
## 1264 widowed lowskill tenant white female
## 1265 notmarried midskill depchild white male
## 1266 notmarried highskill tenant white female
## 1267 married highskill husband white male
## 1268 married midskill husband white male
## 1269 married clerical husband white male
## 1270 divorce highskill tenant white male
## 1271 notmarried highskill single_occ black female
## 1272 married lowskill husband white male
## 1273 notmarried lowskill tenant white female
## 1274 notmarried highskill tenant black female
## 1275 notmarried lowskill single_occ white female
## 1276 married clerical husband white male
## 1277 married highskill husband white male
## 1278 married highskill husband white male
## 1279 married midskill husband white male
## 1280 married highskill husband white male
## 1281 notmarried highskill tenant white male
## 1282 divorce highskill single_occ Asian_Pac_Islander female
## 1283 married other husband white male
## 1284 notmarried agriculture depchild black male
## 1285 married clerical wife white female
## 1286 married midskill husband white male
## 1287 married highskill husband white male
## 1288 married lowskill husband white male
## 1289 notmarried highskill tenant white male
## 1290 married midskill husband white male
## 1291 married other husband white male
## 1292 widowed lowskill single_occ white female
## 1293 married midskill husband white male
## 1294 married highskill husband black male
## 1295 married lowskill husband white male
## 1296 married midskill husband white male
## 1297 notmarried lowskill tenant white male
## 1298 married highskill husband white male
## 1299 notmarried lowskill depchild black male
## 1300 married midskill husband white male
## 1301 notmarried lowskill tenant black male
## 1302 notmarried lowskill single_occ black female
## 1303 married midskill wife white female
## 1304 married highskill husband white male
## 1305 married highskill husband white male
## 1306 married midskill husband white male
## 1307 notmarried midskill tenant white male
## 1308 notmarried clerical depchild white female
## 1309 married highskill husband white male
## 1310 married midskill wife white female
## 1311 married highskill husband white male
## 1312 notmarried highskill tenant white female
## 1313 notmarried other tenant white female
## 1314 divorce clerical single_occ white female
## 1315 married lowskill husband white male
## 1316 notmarried midskill depchild white female
## 1317 married highskill husband white male
## 1318 notmarried other tenant white male
## 1319 married highskill husband white male
## 1320 married midskill husband white male
## 1321 widowed clerical single_occ white female
## 1322 notmarried clerical depchild white male
## 1323 notmarried highskill tenant white male
## 1324 married midskill husband white male
## 1325 married clerical husband white male
## 1326 notmarried other depchild white male
## 1327 married lowskill husband white male
## 1328 widowed lowskill single_occ Asian_Pac_Islander female
## 1329 notmarried highskill depchild black female
## 1330 notmarried lowskill single_occ black female
## 1331 notmarried midskill relative white male
## 1332 notmarried midskill tenant white female
## 1333 notmarried midskill tenant black male
## 1334 married highskill husband white male
## 1335 married highskill husband white male
## 1336 notmarried highskill tenant white female
## 1337 notmarried highskill tenant white male
## 1338 married highskill husband white male
## 1339 divorce highskill depchild white female
## 1340 married highskill husband white male
## 1341 notmarried highskill single_occ white male
## 1342 married highskill tenant white female
## 1343 divorce clerical tenant white male
## 1344 notmarried clerical depchild white male
## 1345 notmarried highskill tenant other male
## 1346 notmarried clerical depchild white female
## 1347 married highskill husband white male
## 1348 notmarried other depchild white female
## 1349 married highskill husband Asian_Pac_Islander male
## 1350 married other husband white male
## 1351 notmarried highskill tenant white male
## 1352 married midskill husband white male
## 1353 divorce highskill depchild white female
## 1354 married midskill husband white male
## 1355 divorce midskill tenant black male
## 1356 married agriculture husband white male
## 1357 notmarried midskill relative black male
## 1358 notmarried highskill tenant white male
## 1359 notmarried highskill tenant white male
## 1360 notmarried midskill depchild white male
## 1361 notmarried midskill tenant white male
## 1362 married midskill husband white male
## 1363 notmarried lowskill tenant white female
## 1364 divorce highskill single_occ white female
## 1365 notmarried highskill tenant white female
## 1366 divorce highskill single_occ white female
## 1367 married midskill wife black female
## 1368 widowed clerical tenant black female
## 1369 married highskill husband Asian_Pac_Islander male
## 1370 notmarried lowskill depchild white male
## 1371 notmarried lowskill depchild black male
## 1372 married other husband white male
## 1373 notmarried lowskill tenant white female
## 1374 married highskill husband white male
## 1375 notmarried clerical tenant white female
## 1376 divorce highskill single_occ black female
## 1377 married midskill husband white male
## 1378 married agriculture husband white male
## 1379 married highskill husband white male
## 1380 divorce highskill single_occ white female
## 1381 divorce clerical depchild white male
## 1382 notmarried highskill tenant white male
## 1383 notmarried highskill tenant white male
## 1384 notmarried lowskill depchild white male
## 1385 married highskill husband white male
## 1386 notmarried midskill depchild white male
## 1387 notmarried highskill tenant black male
## 1388 divorce highskill relative white male
## 1389 notmarried other depchild white male
## 1390 notmarried lowskill depchild white male
## 1391 notmarried highskill tenant white female
## 1392 married midskill husband black male
## 1393 divorce highskill single_occ white female
## 1394 notmarried clerical single_occ black female
## 1395 notmarried midskill tenant black male
## 1396 notmarried midskill single_occ white male
## 1397 married midskill husband black male
## 1398 married highskill husband white male
## 1399 married lowskill husband white male
## 1400 married midskill husband white male
## 1401 notmarried clerical tenant white female
## 1402 married highskill husband white male
## 1403 divorce clerical tenant white female
## 1404 married midskill husband white male
## 1405 divorce other single_occ black female
## 1406 married lowskill husband white male
## 1407 divorce highskill depchild white male
## 1408 married midskill wife white female
## 1409 divorce midskill tenant white male
## 1410 married midskill husband white male
## 1411 widowed lowskill single_occ white female
## 1412 married lowskill husband Asian_Pac_Islander male
## 1413 married highskill husband white male
## 1414 married highskill husband white male
## 1415 married highskill husband white male
## 1416 married clerical husband Asian_Pac_Islander male
## 1417 notmarried lowskill tenant white male
## 1418 married highskill husband black male
## 1419 married highskill wife white female
## 1420 notmarried clerical tenant white female
## 1421 notmarried other depchild white male
## 1422 married lowskill husband black male
## 1423 notmarried clerical depchild white female
## 1424 divorce lowskill tenant white male
## 1425 married clerical husband black male
## 1426 divorce midskill single_occ native male
## 1427 married agriculture husband white male
## 1428 married lowskill wife black female
## 1429 notmarried midskill tenant white male
## 1430 married midskill husband white male
## 1431 married midskill wife white female
## 1432 divorce highskill tenant white female
## 1433 divorce highskill tenant white male
## 1434 notmarried other depchild white female
## 1435 notmarried highskill tenant black male
## 1436 married midskill wife white female
## 1437 notmarried lowskill tenant white male
## 1438 married clerical wife white female
## 1439 divorce highskill single_occ white female
## 1440 married highskill husband white male
## 1441 married midskill husband white male
## 1442 married other husband white male
## 1443 married highskill husband white male
## 1444 divorce midskill tenant white male
## 1445 married lowskill husband white male
## 1446 married midskill husband white male
## 1447 married highskill husband white male
## 1448 married highskill husband white male
## 1449 married highskill husband white male
## 1450 married highskill husband white male
## 1451 divorce midskill tenant white male
## 1452 widowed midskill single_occ black male
## 1453 notmarried other depchild white male
## 1454 married highskill husband white male
## 1455 married midskill husband black male
## 1456 widowed clerical single_occ black female
## 1457 divorce midskill tenant white male
## 1458 notmarried midskill tenant white male
## 1459 widowed other tenant black female
## 1460 married highskill husband white male
## 1461 married highskill husband white male
## 1462 notmarried midskill tenant black male
## 1463 notmarried midskill tenant white male
## 1464 married midskill husband white male
## 1465 divorce midskill tenant native male
## 1466 married clerical husband black male
## 1467 married midskill husband white male
## 1468 divorce highskill tenant white female
## 1469 notmarried highskill tenant white male
## 1470 married highskill husband white male
## 1471 married midskill husband white male
## 1472 notmarried highskill depchild Asian_Pac_Islander male
## 1473 notmarried highskill tenant white female
## 1474 married highskill husband white male
## 1475 notmarried lowskill depchild white female
## 1476 married clerical husband white male
## 1477 divorce lowskill single_occ white female
## 1478 notmarried clerical depchild white female
## 1479 notmarried clerical depchild Asian_Pac_Islander female
## 1480 notmarried lowskill tenant white male
## 1481 married midskill tenant white male
## 1482 married midskill husband white male
## 1483 married highskill husband white male
## 1484 notmarried midskill tenant white male
## 1485 married highskill husband black male
## 1486 married agriculture husband white male
## 1487 notmarried clerical single_occ white male
## 1488 notmarried lowskill depchild white female
## 1489 divorce clerical single_occ white female
## 1490 divorce highskill single_occ white male
## 1491 married highskill husband white male
## 1492 divorce highskill tenant white female
## 1493 married clerical husband white male
## 1494 married clerical husband white male
## 1495 notmarried lowskill relative native male
## 1496 married midskill husband white male
## 1497 divorce highskill tenant white male
## 1498 divorce highskill tenant white female
## 1499 married highskill husband white male
## 1500 notmarried clerical tenant white male
## 1501 married highskill husband white male
## 1502 divorce midskill tenant white male
## 1503 notmarried lowskill tenant white male
## 1504 married highskill husband white male
## 1505 married other husband white male
## 1506 married midskill husband white male
## 1507 married highskill husband white male
## 1508 married highskill husband white male
## 1509 notmarried clerical tenant black female
## 1510 married clerical husband black male
## 1511 married highskill single_occ black male
## 1512 married highskill husband white male
## 1513 married highskill husband white male
## 1514 married highskill husband white male
## 1515 married highskill husband white male
## 1516 divorce lowskill tenant Asian_Pac_Islander male
## 1517 married clerical husband white male
## 1518 married clerical wife white female
## 1519 notmarried highskill depchild white male
## 1520 notmarried lowskill depchild black male
## 1521 married highskill husband white male
## 1522 divorce lowskill tenant white female
## 1523 divorce highskill single_occ white female
## 1524 notmarried agriculture depchild white female
## 1525 notmarried highskill tenant white male
## 1526 notmarried lowskill tenant black male
## 1527 divorce lowskill single_occ white male
## 1528 married midskill husband white male
## 1529 married highskill husband white male
## 1530 divorce highskill single_occ white female
## 1531 married midskill husband black male
## 1532 married midskill husband white male
## 1533 divorce highskill single_occ white female
## 1534 married midskill husband black male
## 1535 divorce lowskill tenant white female
## 1536 notmarried highskill tenant white female
## 1537 married highskill husband white male
## 1538 married midskill husband white male
## 1539 married midskill wife white female
## 1540 divorce lowskill tenant black male
## 1541 divorce clerical depchild white female
## 1542 married highskill husband white male
## 1543 divorce highskill tenant white male
## 1544 married midskill husband white male
## 1545 divorce other tenant white male
## 1546 married midskill husband white male
## 1547 notmarried highskill depchild white male
## 1548 married clerical husband white male
## 1549 married midskill husband white male
## 1550 notmarried midskill single_occ white female
## 1551 notmarried clerical depchild white female
## 1552 notmarried lowskill depchild white male
## 1553 divorce midskill tenant white female
## 1554 notmarried clerical tenant white female
## 1555 married highskill husband white male
## 1556 divorce midskill single_occ black male
## 1557 notmarried clerical tenant white male
## 1558 notmarried highskill tenant white male
## 1559 notmarried midskill tenant white male
## 1560 notmarried lowskill relative black female
## 1561 notmarried lowskill depchild white male
## 1562 married midskill husband white male
## 1563 widowed other tenant white female
## 1564 married highskill husband white male
## 1565 divorce lowskill relative black female
## 1566 notmarried lowskill tenant black female
## 1567 married agriculture husband white male
## 1568 notmarried lowskill tenant black male
## 1569 married midskill husband white male
## 1570 notmarried other depchild white male
## 1571 notmarried other depchild black female
## 1572 married midskill husband white male
## 1573 notmarried lowskill depchild white male
## 1574 notmarried lowskill depchild white male
## 1575 divorce other depchild white male
## 1576 married clerical husband white male
## 1577 notmarried agriculture tenant white male
## 1578 notmarried highskill tenant white male
## 1579 notmarried highskill tenant white male
## 1580 widowed other tenant white female
## 1581 married highskill husband white male
## 1582 notmarried highskill depchild Asian_Pac_Islander male
## 1583 married highskill husband white male
## 1584 divorce clerical tenant white male
## 1585 married agriculture husband white male
## 1586 married midskill husband white male
## 1587 divorce highskill single_occ white male
## 1588 divorce midskill depchild white male
## 1589 notmarried highskill depchild white female
## 1590 notmarried clerical tenant white female
## 1591 married highskill husband white male
## 1592 married highskill husband white male
## 1593 notmarried other depchild white male
## 1594 married highskill husband white male
## 1595 married lowskill husband white male
## 1596 notmarried highskill tenant white male
## 1597 married clerical wife white female
## 1598 notmarried highskill tenant black female
## 1599 notmarried midskill depchild white male
## 1600 notmarried highskill tenant white male
## 1601 married midskill husband white male
## 1602 divorce highskill tenant white female
## 1603 notmarried highskill depchild white female
## 1604 married midskill husband black male
## 1605 married highskill husband white male
## 1606 married clerical husband white male
## 1607 notmarried other single_occ black female
## 1608 divorce midskill depchild white male
## 1609 married midskill husband white male
## 1610 divorce lowskill single_occ Asian_Pac_Islander male
## 1611 divorce highskill tenant white female
## 1612 married highskill wife white female
## 1613 married highskill husband white male
## 1614 married midskill husband white male
## 1615 notmarried clerical depchild white male
## 1616 married highskill husband white male
## 1617 married highskill husband white male
## 1618 married highskill wife white female
## 1619 notmarried midskill relative white male
## 1620 married midskill husband white male
## 1621 married highskill husband white male
## 1622 married highskill husband white male
## 1623 notmarried lowskill depchild Asian_Pac_Islander female
## 1624 married highskill husband white male
## 1625 notmarried lowskill tenant other female
## 1626 married highskill husband white male
## 1627 married midskill husband white male
## 1628 married highskill wife white female
## 1629 married midskill wife white female
## 1630 married other wife black female
## 1631 married lowskill husband white male
## 1632 divorce lowskill tenant white female
## 1633 married clerical husband white male
## 1634 notmarried agriculture tenant white male
## 1635 married midskill husband white male
## 1636 married highskill husband white male
## 1637 divorce lowskill tenant white female
## 1638 notmarried highskill tenant white female
## 1639 married highskill husband white male
## 1640 married midskill husband white male
## 1641 divorce lowskill relative black female
## 1642 married highskill husband white male
## 1643 married highskill husband white male
## 1644 married highskill husband white male
## 1645 married highskill wife white female
## 1646 married clerical wife white female
## 1647 married midskill husband black male
## 1648 married highskill husband white male
## 1649 married highskill husband white male
## 1650 divorce clerical tenant black female
## 1651 notmarried lowskill depchild white male
## 1652 married midskill husband white male
## 1653 notmarried highskill depchild white male
## 1654 married highskill husband white male
## 1655 married midskill husband black male
## 1656 married highskill husband white male
## 1657 divorce other single_occ white female
## 1658 married midskill husband white male
## 1659 notmarried highskill depchild white male
## 1660 married midskill husband white male
## 1661 married midskill husband white male
## 1662 married highskill husband white male
## 1663 notmarried highskill tenant white male
## 1664 married highskill husband white male
## 1665 married lowskill husband black male
## 1666 divorce other single_occ white female
## 1667 married highskill husband white male
## 1668 notmarried highskill tenant white male
## 1669 married clerical husband white male
## 1670 married highskill husband white male
## 1671 married highskill husband white male
## 1672 notmarried highskill single_occ white male
## 1673 married midskill husband white male
## 1674 notmarried highskill depchild white male
## 1675 married clerical wife white female
## 1676 divorce highskill single_occ white male
## 1677 married other husband white male
## 1678 notmarried midskill tenant white male
## 1679 married midskill husband white male
## 1680 married midskill husband white male
## 1681 divorce lowskill single_occ white female
## 1682 married midskill husband white male
## 1683 married highskill husband white male
## 1684 married midskill wife white female
## 1685 notmarried midskill depchild white male
## 1686 married highskill husband white male
## 1687 notmarried lowskill tenant black female
## 1688 married highskill husband white male
## 1689 divorce lowskill tenant white female
## 1690 married highskill husband white male
## 1691 notmarried lowskill tenant white female
## 1692 notmarried clerical depchild white female
## 1693 notmarried highskill depchild white male
## 1694 married highskill wife white female
## 1695 married highskill husband white male
## 1696 married midskill husband black male
## 1697 married highskill wife white female
## 1698 married lowskill husband white male
## 1699 divorce midskill tenant white male
## 1700 married highskill husband white male
## 1701 divorce clerical single_occ black female
## 1702 notmarried lowskill depchild white female
## 1703 married lowskill husband white male
## 1704 divorce midskill tenant white female
## 1705 divorce other tenant white female
## 1706 notmarried midskill tenant white male
## 1707 married midskill husband white male
## 1708 widowed other tenant white female
## 1709 notmarried midskill tenant Asian_Pac_Islander male
## 1710 married highskill husband white male
## 1711 widowed highskill relative white female
## 1712 married midskill husband white male
## 1713 married midskill husband white male
## 1714 married highskill husband white male
## 1715 married midskill husband black male
## 1716 divorce clerical single_occ native female
## 1717 divorce clerical tenant white female
## 1718 married highskill husband Asian_Pac_Islander male
## 1719 married highskill husband white male
## 1720 married midskill husband white male
## 1721 divorce clerical tenant white female
## 1722 married highskill husband white male
## 1723 divorce highskill tenant white male
## 1724 married highskill husband white male
## 1725 notmarried lowskill tenant white female
## 1726 divorce midskill tenant white male
## 1727 married midskill husband white male
## 1728 divorce highskill tenant white male
## 1729 married highskill wife white female
## 1730 married lowskill husband white male
## 1731 notmarried highskill depchild white male
## 1732 notmarried highskill tenant black male
## 1733 notmarried highskill tenant black male
## 1734 notmarried lowskill tenant white male
## 1735 notmarried highskill tenant white male
## 1736 married highskill husband white male
## 1737 notmarried midskill relative white male
## 1738 notmarried lowskill tenant white female
## 1739 divorce lowskill tenant white male
## 1740 married midskill husband white male
## 1741 married clerical husband white male
## 1742 married lowskill wife white female
## 1743 divorce midskill tenant white female
## 1744 notmarried clerical depchild white female
## 1745 married midskill husband white male
## 1746 widowed highskill tenant white female
## 1747 notmarried highskill depchild white male
## 1748 notmarried clerical tenant white female
## 1749 notmarried lowskill tenant white female
## 1750 widowed clerical single_occ white female
## 1751 notmarried midskill tenant white female
## 1752 divorce midskill tenant white male
## 1753 divorce clerical single_occ white female
## 1754 divorce midskill tenant white female
## 1755 married highskill husband black male
## 1756 married highskill depchild white female
## 1757 divorce highskill tenant Asian_Pac_Islander male
## 1758 notmarried agriculture tenant white male
## 1759 married other husband white male
## 1760 notmarried lowskill depchild white female
## 1761 married highskill wife white female
## 1762 married other husband white male
## 1763 divorce clerical single_occ white female
## 1764 notmarried midskill depchild black male
## 1765 divorce highskill tenant white female
## 1766 married highskill wife white female
## 1767 notmarried lowskill relative black male
## 1768 married midskill husband white male
## 1769 married highskill husband white male
## 1770 married highskill husband white male
## 1771 married highskill tenant white male
## 1772 married highskill husband white male
## 1773 married midskill husband white male
## 1774 notmarried other depchild white female
## 1775 married lowskill wife black female
## 1776 married midskill husband white male
## 1777 married midskill husband white male
## 1778 widowed agriculture single_occ white male
## 1779 notmarried other depchild white male
## 1780 married highskill husband white male
## 1781 married highskill husband white male
## 1782 married highskill husband white male
## 1783 notmarried lowskill depchild white male
## 1784 married highskill husband white male
## 1785 notmarried clerical tenant white male
## 1786 married highskill tenant white female
## 1787 married highskill wife white female
## 1788 married highskill husband Asian_Pac_Islander male
## 1789 divorce clerical single_occ white female
## 1790 married lowskill single_occ white female
## 1791 married highskill husband white male
## 1792 divorce midskill tenant white male
## 1793 notmarried lowskill single_occ white female
## 1794 notmarried clerical tenant Asian_Pac_Islander female
## 1795 married midskill husband black male
## 1796 married highskill husband white male
## 1797 notmarried clerical tenant white male
## 1798 married lowskill husband black male
## 1799 married clerical husband white male
## 1800 divorce highskill tenant white female
## 1801 married clerical husband white male
## 1802 widowed clerical single_occ black female
## 1803 divorce lowskill tenant white female
## 1804 married highskill husband Asian_Pac_Islander male
## 1805 notmarried highskill tenant white male
## 1806 notmarried highskill tenant white male
## 1807 married midskill husband white male
## 1808 married highskill husband white male
## 1809 married highskill husband white male
## 1810 divorce highskill single_occ white male
## 1811 married highskill husband white male
## 1812 married highskill husband white male
## 1813 married midskill husband white male
## 1814 widowed highskill depchild black female
## 1815 married highskill husband white male
## 1816 notmarried lowskill depchild black male
## 1817 married midskill husband white male
## 1818 divorce midskill single_occ white female
## 1819 married midskill husband white male
## 1820 married clerical husband white male
## 1821 married lowskill husband white male
## 1822 notmarried midskill tenant white male
## 1823 notmarried highskill depchild white male
## 1824 notmarried other depchild white male
## 1825 married midskill husband white male
## 1826 divorce highskill single_occ white male
## 1827 notmarried highskill single_occ white female
## 1828 divorce clerical single_occ black female
## 1829 notmarried highskill depchild white female
## 1830 divorce midskill single_occ white female
## 1831 notmarried lowskill depchild white male
## 1832 widowed highskill tenant white female
## 1833 married highskill husband white male
## 1834 divorce highskill tenant white female
## 1835 notmarried midskill tenant white male
## 1836 notmarried clerical tenant white female
## 1837 married highskill husband white male
## 1838 married agriculture husband white male
## 1839 divorce clerical single_occ white male
## 1840 married clerical husband Asian_Pac_Islander male
## 1841 notmarried midskill tenant white male
## 1842 married midskill husband white male
## 1843 married midskill wife white female
## 1844 married midskill husband white male
## 1845 notmarried midskill tenant native male
## 1846 notmarried midskill tenant black male
## 1847 notmarried other depchild white female
## 1848 notmarried lowskill depchild white female
## 1849 notmarried highskill tenant white female
## 1850 married midskill husband white male
## 1851 married lowskill husband white male
## 1852 married other husband white male
## 1853 notmarried lowskill depchild black male
## 1854 divorce midskill single_occ white male
## 1855 notmarried midskill single_occ black male
## 1856 notmarried highskill depchild white female
## 1857 notmarried highskill tenant white female
## 1858 notmarried lowskill depchild white female
## 1859 notmarried lowskill depchild white male
## 1860 married clerical wife white female
## 1861 divorce highskill tenant white male
## 1862 married midskill husband white male
## 1863 married highskill husband white male
## 1864 notmarried midskill relative white male
## 1865 married highskill husband Asian_Pac_Islander male
## 1866 married other single_occ black female
## 1867 notmarried lowskill tenant white male
## 1868 divorce clerical tenant white female
## 1869 divorce lowskill tenant white male
## 1870 married highskill husband white male
## 1871 married midskill husband white male
## 1872 notmarried clerical depchild black male
## 1873 married highskill husband white male
## 1874 notmarried clerical tenant white male
## 1875 notmarried lowskill depchild white male
## 1876 married highskill husband black male
## 1877 divorce highskill tenant white male
## 1878 married highskill husband white male
## 1879 notmarried other depchild white male
## 1880 divorce clerical tenant white female
## 1881 notmarried lowskill single_occ white male
## 1882 married highskill husband white male
## 1883 married highskill wife white female
## 1884 notmarried highskill tenant black male
## 1885 notmarried highskill single_occ white male
## 1886 notmarried lowskill tenant white female
## 1887 notmarried highskill tenant white female
## 1888 married agriculture husband white male
## 1889 notmarried lowskill depchild white male
## 1890 married highskill wife white female
## 1891 married agriculture husband white male
## 1892 widowed highskill tenant white female
## 1893 divorce lowskill relative white male
## 1894 married highskill husband white male
## 1895 widowed highskill tenant white male
## 1896 divorce highskill tenant white male
## 1897 married highskill husband white male
## 1898 notmarried midskill tenant white male
## 1899 married midskill husband white male
## 1900 notmarried highskill depchild white male
## 1901 notmarried lowskill tenant other male
## 1902 widowed agriculture single_occ white female
## 1903 notmarried midskill tenant white male
## 1904 notmarried clerical tenant white female
## 1905 married midskill husband white male
## 1906 married midskill husband white male
## 1907 divorce highskill single_occ white female
## 1908 notmarried highskill tenant white female
## 1909 married midskill husband white male
## 1910 married midskill husband white male
## 1911 married midskill husband white male
## 1912 notmarried midskill single_occ white male
## 1913 married highskill husband white male
## 1914 married midskill husband white male
## 1915 divorce midskill tenant white male
## 1916 married highskill husband white male
## 1917 married highskill husband white male
## 1918 married highskill husband black male
## 1919 notmarried highskill depchild white male
## 1920 notmarried clerical tenant white female
## 1921 divorce clerical single_occ white female
## 1922 married midskill husband black male
## 1923 married highskill husband white male
## 1924 notmarried other depchild white female
## 1925 notmarried highskill depchild white female
## 1926 notmarried highskill tenant white male
## 1927 notmarried highskill tenant white male
## 1928 married lowskill husband white male
## 1929 notmarried midskill depchild white male
## 1930 notmarried highskill tenant white male
## 1931 divorce clerical tenant white male
## 1932 married other depchild black female
## 1933 married midskill relative Asian_Pac_Islander female
## 1934 married highskill relative white female
## 1935 married highskill husband white male
## 1936 married highskill husband white male
## 1937 divorce highskill single_occ black male
## 1938 married highskill single_occ white female
## 1939 married highskill husband white male
## 1940 notmarried clerical tenant white female
## 1941 married agriculture husband white male
## 1942 married highskill husband white male
## 1943 divorce highskill single_occ black female
## 1944 married clerical wife white female
## 1945 notmarried clerical relative white female
## 1946 married highskill husband white male
## 1947 divorce highskill tenant white female
## 1948 notmarried clerical single_occ black female
## 1949 married highskill husband white male
## 1950 married highskill husband white male
## 1951 married highskill husband white male
## 1952 married highskill husband white male
## 1953 notmarried highskill tenant black male
## 1954 married midskill husband white male
## 1955 married highskill husband white male
## 1956 notmarried midskill depchild white male
## 1957 married highskill husband white male
## 1958 widowed lowskill tenant white female
## 1959 notmarried highskill tenant white female
## 1960 married highskill husband white male
## 1961 notmarried clerical depchild white male
## 1962 married highskill husband white male
## 1963 married highskill husband white male
## 1964 married highskill husband white male
## 1965 notmarried midskill single_occ white male
## 1966 divorce lowskill single_occ white female
## 1967 notmarried highskill depchild white male
## 1968 notmarried clerical single_occ black female
## 1969 married midskill husband white male
## 1970 married highskill husband Asian_Pac_Islander male
## 1971 notmarried highskill tenant white male
## 1972 divorce other tenant black male
## 1973 notmarried highskill tenant white male
## 1974 married lowskill husband white male
## 1975 married highskill husband white male
## 1976 married highskill tenant Asian_Pac_Islander male
## 1977 notmarried highskill tenant white male
## 1978 notmarried highskill tenant white female
## 1979 married agriculture husband white male
## 1980 divorce lowskill depchild black male
## 1981 divorce highskill tenant white female
## 1982 married clerical husband white male
## 1983 notmarried highskill tenant black female
## 1984 married midskill husband white male
## 1985 married highskill husband white male
## 1986 notmarried midskill tenant white male
## 1987 married highskill husband white male
## 1988 married other wife white female
## 1989 married midskill husband white male
## 1990 notmarried highskill tenant black female
## 1991 notmarried lowskill tenant white female
## 1992 notmarried lowskill tenant white male
## 1993 notmarried highskill tenant white male
## 1994 divorce highskill single_occ white male
## 1995 notmarried highskill single_occ white female
## 1996 divorce midskill tenant white male
## 1997 married highskill husband white male
## 1998 married highskill tenant white female
## 1999 married highskill husband white male
## 2000 divorce midskill single_occ white male
## 2001 notmarried midskill single_occ white male
## 2002 notmarried highskill relative white female
## 2003 married agriculture husband white male
## 2004 married highskill wife Asian_Pac_Islander female
## 2005 divorce highskill tenant white male
## 2006 notmarried agriculture depchild black male
## 2007 notmarried clerical relative black female
## 2008 married midskill husband white male
## 2009 notmarried midskill depchild white male
## 2010 married lowskill tenant white female
## 2011 notmarried highskill tenant white female
## 2012 notmarried lowskill depchild white female
## 2013 notmarried lowskill tenant white female
## 2014 notmarried highskill tenant white female
## 2015 married highskill husband white male
## 2016 notmarried highskill tenant white female
## 2017 notmarried highskill tenant white male
## 2018 notmarried clerical tenant white male
## 2019 married highskill husband white male
## 2020 notmarried midskill depchild white male
## 2021 married lowskill wife white female
## 2022 notmarried highskill tenant white male
## 2023 divorce lowskill relative white male
## 2024 divorce lowskill depchild white female
## 2025 married highskill husband white male
## 2026 notmarried other tenant white female
## 2027 married midskill husband white male
## 2028 notmarried lowskill tenant white male
## 2029 married highskill husband white male
## 2030 married highskill husband white male
## 2031 married highskill wife white female
## 2032 married highskill wife white female
## 2033 married midskill husband white male
## 2034 married midskill husband black male
## 2035 married highskill depchild black male
## 2036 divorce highskill single_occ white female
## 2037 notmarried other relative native female
## 2038 married midskill husband white male
## 2039 married highskill husband white male
## 2040 married highskill husband white male
## 2041 notmarried highskill single_occ white male
## 2042 widowed lowskill single_occ black female
## 2043 married agriculture husband white male
## 2044 married highskill husband white male
## 2045 divorce highskill tenant white male
## 2046 notmarried clerical depchild white male
## 2047 notmarried other tenant white male
## 2048 notmarried midskill tenant white male
## 2049 married midskill husband white male
## 2050 married highskill depchild white female
## 2051 notmarried clerical tenant white female
## 2052 married highskill husband white male
## 2053 notmarried lowskill relative black male
## 2054 notmarried highskill single_occ white male
## 2055 notmarried highskill tenant white female
## 2056 married midskill husband white male
## 2057 notmarried highskill tenant white male
## 2058 married midskill husband white male
## 2059 notmarried lowskill depchild white male
## 2060 notmarried clerical depchild white female
## 2061 divorce lowskill tenant Asian_Pac_Islander female
## 2062 notmarried other depchild white male
## 2063 notmarried highskill depchild white female
## 2064 married highskill husband white male
## 2065 divorce clerical tenant white female
## 2066 notmarried lowskill single_occ white female
## 2067 married highskill husband white male
## 2068 widowed clerical tenant white female
## 2069 married lowskill wife Asian_Pac_Islander female
## 2070 divorce highskill depchild white male
## 2071 notmarried highskill tenant white male
## 2072 married agriculture husband white male
## 2073 married other husband white male
## 2074 married highskill husband black male
## 2075 married highskill husband white male
## 2076 notmarried clerical depchild black male
## 2077 divorce lowskill single_occ white female
## 2078 notmarried midskill depchild white male
## 2079 notmarried agriculture depchild white male
## 2080 married highskill husband white male
## 2081 divorce highskill single_occ black female
## 2082 married highskill husband white male
## 2083 divorce highskill single_occ white female
## 2084 divorce midskill tenant white male
## 2085 married other wife white female
## 2086 married highskill husband white male
## 2087 married highskill husband white male
## 2088 notmarried clerical depchild white male
## 2089 married clerical wife white female
## 2090 married lowskill husband white male
## 2091 married highskill husband white male
## 2092 notmarried other tenant white male
## 2093 divorce highskill tenant white male
## 2094 married highskill husband white male
## 2095 notmarried other depchild white male
## 2096 married midskill husband white male
## 2097 notmarried highskill tenant Asian_Pac_Islander male
## 2098 divorce clerical tenant white female
## 2099 married highskill husband white male
## 2100 married midskill husband white male
## 2101 divorce highskill tenant white female
## 2102 married midskill husband white male
## 2103 divorce midskill single_occ white female
## 2104 married highskill husband white male
## 2105 married midskill husband Asian_Pac_Islander male
## 2106 notmarried other depchild white male
## 2107 married midskill husband white male
## 2108 married midskill relative black female
## 2109 married agriculture husband white male
## 2110 married highskill husband white male
## 2111 married highskill husband white male
## 2112 divorce clerical single_occ white female
## 2113 married clerical wife white female
## 2114 notmarried highskill single_occ white female
## 2115 notmarried highskill depchild white male
## 2116 notmarried highskill depchild white female
## 2117 divorce midskill tenant white male
## 2118 divorce highskill tenant white female
## 2119 widowed other tenant white female
## 2120 notmarried midskill depchild white male
## 2121 married clerical wife white female
## 2122 notmarried midskill depchild white female
## 2123 notmarried highskill depchild black male
## 2124 notmarried highskill depchild white male
## 2125 notmarried lowskill depchild white female
## 2126 notmarried highskill depchild white male
## 2127 notmarried other depchild Asian_Pac_Islander female
## 2128 notmarried midskill tenant white male
## 2129 married lowskill wife black female
## 2130 married highskill husband white male
## 2131 divorce clerical single_occ Asian_Pac_Islander female
## 2132 divorce highskill single_occ white female
## 2133 notmarried clerical tenant white female
## 2134 notmarried highskill tenant white female
## 2135 married midskill husband white male
## 2136 notmarried highskill tenant Asian_Pac_Islander female
## 2137 notmarried lowskill tenant white female
## 2138 divorce clerical relative white female
## 2139 notmarried lowskill tenant white female
## 2140 married highskill husband white male
## 2141 widowed highskill single_occ white female
## 2142 widowed highskill single_occ white male
## 2143 widowed clerical single_occ white female
## 2144 divorce highskill depchild white female
## 2145 married midskill husband white male
## 2146 married midskill husband white male
## 2147 notmarried highskill depchild white female
## 2148 married midskill husband white male
## 2149 divorce highskill tenant white male
## 2150 married midskill husband white male
## 2151 notmarried midskill tenant black male
## 2152 notmarried clerical tenant white male
## 2153 notmarried other depchild white male
## 2154 married highskill husband white male
## 2155 widowed lowskill tenant white female
## 2156 married other husband white male
## 2157 married midskill husband black male
## 2158 married highskill husband white male
## 2159 married highskill husband white male
## 2160 married highskill husband white male
## 2161 married highskill husband white male
## 2162 notmarried lowskill tenant white female
## 2163 notmarried midskill depchild white male
## 2164 married other husband white male
## 2165 married other wife white female
## 2166 notmarried lowskill depchild black female
## 2167 married highskill husband white male
## 2168 married highskill husband white male
## 2169 divorce highskill single_occ white male
## 2170 divorce lowskill tenant black female
## 2171 married midskill husband white male
## 2172 divorce highskill depchild white female
## 2173 married midskill husband white male
## 2174 married midskill husband white male
## 2175 married highskill husband white male
## 2176 married highskill wife white female
## 2177 married midskill husband white male
## 2178 divorce clerical tenant white female
## 2179 married midskill husband white male
## 2180 married highskill husband white male
## 2181 married highskill wife white female
## 2182 married highskill husband white male
## 2183 divorce highskill single_occ white male
## 2184 married agriculture husband white male
## 2185 married highskill husband white male
## 2186 divorce midskill relative white male
## 2187 divorce highskill tenant white male
## 2188 notmarried midskill tenant white male
## 2189 notmarried highskill tenant white male
## 2190 widowed midskill tenant white female
## 2191 married midskill husband white male
## 2192 notmarried clerical tenant white female
## 2193 divorce clerical depchild black female
## 2194 married lowskill relative white male
## 2195 married highskill husband white male
## 2196 married midskill husband white male
## 2197 married midskill husband white male
## 2198 notmarried clerical depchild white female
## 2199 married clerical wife white female
## 2200 married midskill husband white male
## 2201 married agriculture husband white male
## 2202 married clerical husband white male
## 2203 widowed clerical single_occ white female
## 2204 notmarried highskill depchild white female
## 2205 divorce highskill single_occ white female
## 2206 married midskill wife black female
## 2207 married midskill husband white male
## 2208 notmarried lowskill tenant black female
## 2209 married highskill husband Asian_Pac_Islander male
## 2210 widowed highskill tenant black male
## 2211 divorce other tenant white female
## 2212 notmarried lowskill tenant white female
## 2213 married lowskill husband white male
## 2214 married other husband white male
## 2215 notmarried lowskill tenant white male
## 2216 married highskill husband white male
## 2217 notmarried clerical tenant white female
## 2218 married highskill husband white male
## 2219 notmarried midskill single_occ black female
## 2220 divorce highskill tenant white female
## 2221 notmarried midskill tenant white male
## 2222 divorce lowskill tenant white female
## 2223 notmarried other depchild white female
## 2224 notmarried midskill depchild black male
## 2225 notmarried midskill single_occ white male
## 2226 notmarried highskill tenant white male
## 2227 divorce clerical single_occ white female
## 2228 divorce highskill tenant white female
## 2229 married clerical wife black female
## 2230 married midskill husband white male
## 2231 married highskill husband white male
## 2232 notmarried highskill depchild white female
## 2233 notmarried highskill tenant black male
## 2234 married highskill husband white male
## 2235 married midskill husband white male
## 2236 divorce highskill single_occ white female
## 2237 married highskill wife white female
## 2238 married highskill husband white male
## 2239 notmarried highskill single_occ white female
## 2240 married clerical husband white male
## 2241 married highskill husband white male
## 2242 married midskill husband white male
## 2243 married lowskill husband white male
## 2244 married midskill husband white male
## 2245 divorce highskill single_occ white female
## 2246 married midskill husband white male
## 2247 divorce highskill tenant white male
## 2248 divorce clerical tenant white female
## 2249 notmarried lowskill depchild white male
## 2250 married clerical husband Asian_Pac_Islander male
## 2251 divorce highskill single_occ white female
## 2252 notmarried clerical single_occ white female
## 2253 married clerical husband white male
## 2254 notmarried highskill tenant white female
## 2255 married highskill husband white male
## 2256 divorce lowskill tenant black male
## 2257 married agriculture husband white male
## 2258 notmarried lowskill depchild white female
## 2259 married highskill husband white male
## 2260 notmarried highskill single_occ Asian_Pac_Islander male
## 2261 notmarried midskill depchild white male
## 2262 married midskill relative white male
## 2263 divorce clerical tenant white female
## 2264 divorce midskill tenant white male
## 2265 notmarried highskill depchild white male
## 2266 married highskill husband white male
## 2267 notmarried midskill depchild white male
## 2268 notmarried midskill single_occ black female
## 2269 married midskill husband white male
## 2270 married highskill husband white male
## 2271 divorce midskill single_occ white male
## 2272 notmarried midskill tenant white male
## 2273 notmarried highskill depchild white female
## 2274 married highskill husband white male
## 2275 married midskill husband white male
## 2276 married midskill husband white male
## 2277 widowed lowskill depchild white female
## 2278 married midskill husband white male
## 2279 notmarried highskill relative white female
## 2280 notmarried highskill tenant white female
## 2281 notmarried highskill tenant white female
## 2282 notmarried other depchild white male
## 2283 married lowskill husband white male
## 2284 notmarried highskill single_occ white female
## 2285 notmarried highskill tenant white male
## 2286 notmarried highskill tenant white female
## 2287 married highskill husband Asian_Pac_Islander male
## 2288 married highskill husband white male
## 2289 married highskill relative black female
## 2290 married midskill husband white male
## 2291 notmarried lowskill depchild white female
## 2292 divorce midskill tenant white male
## 2293 notmarried other depchild white male
## 2294 married midskill husband white male
## 2295 divorce midskill single_occ white female
## 2296 notmarried midskill depchild white male
## 2297 notmarried midskill relative white male
## 2298 divorce clerical single_occ white female
## 2299 married lowskill husband white male
## 2300 divorce clerical single_occ white female
## 2301 divorce highskill tenant white male
## 2302 divorce highskill tenant white female
## 2303 divorce highskill tenant white male
## 2304 notmarried lowskill tenant Asian_Pac_Islander male
## 2305 married highskill husband white male
## 2306 married lowskill wife white female
## 2307 married midskill husband white male
## 2308 notmarried lowskill depchild white male
## 2309 married highskill husband white male
## 2310 widowed clerical tenant white female
## 2311 widowed midskill tenant white male
## 2312 married highskill tenant white male
## 2313 married highskill wife white female
## 2314 notmarried clerical depchild native female
## 2315 married clerical wife white female
## 2316 widowed midskill tenant white male
## 2317 married highskill husband white male
## 2318 notmarried highskill tenant white male
## 2319 married highskill husband white male
## 2320 married highskill husband white male
## 2321 notmarried midskill tenant white male
## 2322 married highskill husband white male
## 2323 notmarried midskill depchild white male
## 2324 notmarried other depchild Asian_Pac_Islander male
## 2325 married highskill husband white male
## 2326 married highskill husband white male
## 2327 widowed midskill single_occ white male
## 2328 married other tenant white male
## 2329 married agriculture husband white male
## 2330 divorce highskill tenant white male
## 2331 married highskill wife Asian_Pac_Islander female
## 2332 divorce highskill tenant white male
## 2333 married midskill husband white male
## 2334 divorce midskill tenant white male
## 2335 married highskill husband white male
## 2336 notmarried clerical depchild white female
## 2337 married midskill tenant white male
## 2338 notmarried midskill depchild white male
## 2339 notmarried highskill depchild white female
## 2340 married highskill husband Asian_Pac_Islander male
## 2341 divorce other tenant white female
## 2342 married midskill husband white male
## 2343 notmarried agriculture tenant white male
## 2344 notmarried lowskill single_occ black female
## 2345 married highskill husband white male
## 2346 married highskill husband white male
## 2347 married highskill husband white male
## 2348 notmarried lowskill depchild white male
## 2349 married midskill husband white male
## 2350 notmarried highskill tenant black male
## 2351 divorce highskill tenant white female
## 2352 notmarried highskill tenant white female
## 2353 married highskill husband white male
## 2354 married agriculture husband white male
## 2355 notmarried other depchild white female
## 2356 married highskill husband white male
## 2357 divorce other depchild white male
## 2358 married lowskill husband white male
## 2359 married other husband white male
## 2360 notmarried other depchild white male
## 2361 married highskill husband white male
## 2362 married highskill husband white male
## 2363 married midskill husband white male
## 2364 notmarried lowskill depchild white male
## 2365 notmarried midskill depchild black male
## 2366 divorce lowskill tenant black female
## 2367 notmarried clerical tenant white male
## 2368 divorce highskill tenant white female
## 2369 notmarried midskill depchild white male
## 2370 married lowskill husband white male
## 2371 married highskill husband white male
## 2372 divorce highskill tenant white male
## 2373 married other husband white male
## 2374 married midskill husband white male
## 2375 notmarried highskill tenant white male
## 2376 divorce highskill tenant black female
## 2377 notmarried highskill tenant black female
## 2378 married midskill husband white male
## 2379 divorce clerical single_occ white female
## 2380 divorce highskill tenant white male
## 2381 notmarried other tenant white female
## 2382 notmarried midskill depchild white male
## 2383 notmarried other tenant white male
## 2384 married lowskill single_occ native female
## 2385 notmarried highskill depchild white female
## 2386 married highskill husband white male
## 2387 married highskill husband white male
## 2388 married agriculture husband white male
## 2389 married midskill husband white male
## 2390 married clerical husband white male
## 2391 married highskill husband white male
## 2392 notmarried highskill tenant white male
## 2393 married lowskill husband white male
## 2394 notmarried lowskill tenant white male
## 2395 notmarried highskill depchild white female
## 2396 notmarried midskill tenant white male
## 2397 notmarried highskill tenant white female
## 2398 notmarried other depchild white female
## 2399 notmarried highskill depchild white female
## 2400 divorce midskill tenant white male
## 2401 notmarried clerical tenant black female
## 2402 notmarried midskill depchild native male
## 2403 married lowskill husband white male
## 2404 married highskill husband white male
## 2405 married highskill husband white male
## 2406 married midskill husband white male
## 2407 married highskill husband white male
## 2408 married lowskill wife white female
## 2409 notmarried clerical tenant white male
## 2410 notmarried lowskill depchild white male
## 2411 married lowskill husband white male
## 2412 divorce clerical single_occ white female
## 2413 notmarried highskill depchild Asian_Pac_Islander male
## 2414 notmarried agriculture tenant white male
## 2415 divorce highskill single_occ white male
## 2416 married highskill husband white male
## 2417 married agriculture husband white male
## 2418 notmarried clerical depchild white female
## 2419 notmarried highskill tenant white male
## 2420 married midskill husband white male
## 2421 married other husband white male
## 2422 widowed clerical tenant white female
## 2423 married midskill husband white male
## 2424 notmarried highskill tenant white male
## 2425 married midskill husband white male
## 2426 married midskill husband white male
## 2427 married highskill husband white male
## 2428 notmarried other depchild white male
## 2429 notmarried highskill depchild white female
## 2430 married agriculture husband white male
## 2431 married midskill husband white male
## 2432 married midskill husband white male
## 2433 married highskill husband Asian_Pac_Islander male
## 2434 married highskill husband white male
## 2435 notmarried highskill tenant white male
## 2436 notmarried lowskill single_occ black female
## 2437 notmarried clerical tenant white male
## 2438 notmarried lowskill tenant black male
## 2439 notmarried highskill depchild white female
## 2440 married midskill husband white male
## 2441 married highskill husband white male
## 2442 married midskill husband black male
## 2443 divorce clerical single_occ white female
## 2444 notmarried midskill relative white male
## 2445 notmarried highskill depchild white male
## 2446 married highskill husband native male
## 2447 married lowskill husband white male
## 2448 divorce lowskill tenant white female
## 2449 notmarried highskill tenant black female
## 2450 divorce lowskill depchild white female
## 2451 divorce highskill tenant white female
## 2452 notmarried lowskill single_occ white female
## 2453 notmarried highskill tenant other male
## 2454 married clerical wife white female
## 2455 divorce clerical single_occ white female
## 2456 divorce midskill tenant white female
## 2457 married highskill husband white male
## 2458 married midskill husband white male
## 2459 divorce clerical single_occ white female
## 2460 divorce midskill depchild white male
## 2461 married midskill husband white male
## 2462 notmarried midskill tenant white male
## 2463 married highskill husband white male
## 2464 divorce highskill tenant black male
## 2465 married other husband white male
## 2466 married highskill husband white male
## 2467 divorce midskill tenant white female
## 2468 divorce highskill relative white female
## 2469 divorce highskill tenant white male
## 2470 married highskill wife black female
## 2471 married highskill husband white male
## 2472 notmarried highskill tenant white female
## 2473 married highskill relative white female
## 2474 married highskill husband white male
## 2475 married midskill husband white male
## 2476 notmarried midskill depchild white female
## 2477 notmarried other tenant white male
## 2478 divorce midskill tenant white male
## 2479 married midskill husband white male
## 2480 divorce lowskill single_occ white male
## 2481 married highskill husband white male
## 2482 married highskill husband white male
## 2483 notmarried clerical tenant white male
## 2484 married highskill husband white male
## 2485 married highskill husband white male
## 2486 married midskill husband white male
## 2487 married other husband black male
## 2488 divorce clerical tenant white female
## 2489 married midskill husband native male
## 2490 divorce highskill single_occ white female
## 2491 divorce midskill single_occ white male
## 2492 married other husband white male
## 2493 notmarried lowskill depchild white female
## 2494 notmarried midskill depchild white male
## 2495 divorce highskill tenant white male
## 2496 married midskill husband white male
## 2497 notmarried other depchild white female
## 2498 married highskill husband white male
## 2499 married highskill husband white male
## 2500 notmarried highskill depchild black male
## 2501 notmarried clerical tenant white female
## 2502 married highskill tenant white male
## 2503 divorce midskill tenant white female
## 2504 divorce midskill single_occ white female
## 2505 married midskill husband white male
## 2506 widowed midskill tenant white male
## 2507 notmarried other depchild white male
## 2508 married highskill husband white male
## 2509 notmarried midskill tenant white male
## 2510 married midskill tenant Asian_Pac_Islander male
## 2511 divorce highskill tenant black male
## 2512 notmarried clerical tenant white female
## 2513 married clerical husband Asian_Pac_Islander male
## 2514 married other husband white male
## 2515 married midskill husband white male
## 2516 notmarried lowskill relative black male
## 2517 married lowskill wife white female
## 2518 married highskill husband white male
## 2519 notmarried lowskill depchild black female
## 2520 married midskill husband white male
## 2521 married midskill husband white male
## 2522 notmarried other tenant white female
## 2523 married midskill husband white male
## 2524 divorce lowskill single_occ white female
## 2525 married midskill husband white male
## 2526 divorce midskill tenant white male
## 2527 divorce highskill single_occ Asian_Pac_Islander female
## 2528 notmarried highskill relative white female
## 2529 married highskill husband white male
## 2530 notmarried highskill tenant white male
## 2531 notmarried lowskill tenant white female
## 2532 notmarried lowskill tenant white female
## 2533 notmarried highskill depchild white male
## 2534 notmarried highskill tenant black male
## 2535 married midskill husband white male
## 2536 divorce clerical single_occ white female
## 2537 notmarried clerical tenant white male
## 2538 notmarried highskill tenant white male
## 2539 married midskill husband white male
## 2540 divorce highskill single_occ black female
## 2541 widowed clerical single_occ white female
## 2542 married clerical husband black male
## 2543 married highskill tenant white male
## 2544 widowed midskill single_occ white male
## 2545 notmarried other depchild white female
## 2546 divorce highskill tenant white female
## 2547 married midskill husband white male
## 2548 notmarried clerical depchild Asian_Pac_Islander female
## 2549 divorce clerical tenant white female
## 2550 married highskill tenant white female
## 2551 notmarried lowskill depchild white female
## 2552 married agriculture husband white male
## 2553 married highskill husband white male
## 2554 notmarried clerical single_occ other female
## 2555 married highskill husband white male
## 2556 notmarried agriculture depchild white male
## 2557 married highskill husband white male
## 2558 notmarried highskill tenant white female
## 2559 married highskill husband Asian_Pac_Islander male
## 2560 married clerical wife white female
## 2561 married clerical wife white female
## 2562 notmarried midskill single_occ black female
## 2563 divorce highskill tenant white male
## 2564 notmarried clerical single_occ white female
## 2565 notmarried highskill depchild black male
## 2566 married clerical husband white male
## 2567 married other husband white male
## 2568 notmarried highskill tenant white male
## 2569 notmarried agriculture depchild white male
## 2570 married highskill husband white male
## 2571 married other husband white male
## 2572 notmarried other tenant white female
## 2573 divorce midskill tenant white male
## 2574 divorce midskill tenant white female
## 2575 married highskill husband white male
## 2576 notmarried highskill single_occ black female
## 2577 married highskill husband white male
## 2578 divorce clerical single_occ white female
## 2579 married other husband black male
## 2580 married midskill husband white male
## 2581 married midskill husband white male
## 2582 married midskill single_occ white female
## 2583 married midskill husband white male
## 2584 married highskill husband white male
## 2585 divorce midskill single_occ white male
## 2586 divorce clerical tenant white female
## 2587 notmarried other tenant white female
## 2588 married highskill husband Asian_Pac_Islander male
## 2589 married highskill husband white male
## 2590 married midskill husband white male
## 2591 divorce clerical relative Asian_Pac_Islander female
## 2592 married midskill tenant white male
## 2593 notmarried midskill tenant white male
## 2594 notmarried highskill tenant black male
## 2595 widowed other single_occ white male
## 2596 divorce lowskill single_occ white female
## 2597 notmarried midskill depchild black female
## 2598 divorce highskill tenant white female
## 2599 married midskill husband white male
## 2600 married clerical husband white male
## 2601 notmarried clerical tenant white male
## 2602 married midskill husband native male
## 2603 divorce highskill single_occ white female
## 2604 divorce highskill tenant white female
## 2605 notmarried lowskill depchild white male
## 2606 married lowskill husband other male
## 2607 notmarried other depchild white male
## 2608 married highskill husband white male
## 2609 notmarried lowskill tenant white female
## 2610 married highskill husband white male
## 2611 notmarried highskill relative native female
## 2612 married midskill husband black male
## 2613 married highskill husband white male
## 2614 notmarried midskill relative white male
## 2615 married highskill husband white male
## 2616 widowed clerical tenant black female
## 2617 notmarried midskill tenant white male
## 2618 notmarried clerical tenant white female
## 2619 married highskill husband white male
## 2620 married lowskill husband white male
## 2621 married midskill husband white male
## 2622 notmarried highskill tenant white male
## 2623 married midskill husband native male
## 2624 notmarried midskill tenant white female
## 2625 married midskill husband white male
## 2626 married midskill husband white male
## 2627 married highskill husband white male
## 2628 divorce midskill tenant white male
## 2629 notmarried lowskill relative white male
## 2630 married midskill husband white male
## 2631 notmarried highskill tenant white male
## 2632 married highskill husband white male
## 2633 married other wife white female
## 2634 married highskill husband white male
## 2635 notmarried other tenant white male
## 2636 married highskill husband white male
## 2637 notmarried clerical tenant white female
## 2638 married highskill wife white female
## 2639 married other husband white male
## 2640 notmarried midskill tenant white female
## 2641 married midskill husband white male
## 2642 divorce highskill tenant white female
## 2643 notmarried clerical depchild white female
## 2644 married midskill husband white male
## 2645 notmarried lowskill tenant white female
## 2646 married midskill husband white male
## 2647 widowed lowskill tenant white female
## 2648 married highskill husband other male
## 2649 notmarried clerical tenant white female
## 2650 married clerical wife white female
## 2651 notmarried clerical depchild black female
## 2652 married highskill tenant white male
## 2653 married highskill husband white male
## 2654 notmarried clerical tenant Asian_Pac_Islander male
## 2655 married highskill husband Asian_Pac_Islander male
## 2656 notmarried clerical relative Asian_Pac_Islander female
## 2657 notmarried highskill depchild white female
## 2658 widowed clerical single_occ white female
## 2659 married midskill husband white male
## 2660 married highskill husband white male
## 2661 married midskill husband white male
## 2662 married midskill husband black male
## 2663 divorce midskill single_occ white female
## 2664 divorce highskill single_occ white female
## 2665 divorce highskill tenant white female
## 2666 married highskill husband white male
## 2667 notmarried lowskill depchild white male
## 2668 married lowskill husband white male
## 2669 divorce lowskill depchild white female
## 2670 widowed highskill tenant white female
## 2671 married lowskill husband white male
## 2672 notmarried highskill depchild white male
## 2673 notmarried highskill tenant white male
## 2674 notmarried other depchild white male
## 2675 notmarried lowskill tenant white male
## 2676 married midskill husband white male
## 2677 married other husband white male
## 2678 notmarried clerical single_occ white male
## 2679 notmarried agriculture tenant white female
## 2680 widowed highskill single_occ white female
## 2681 married highskill husband white male
## 2682 married highskill husband white male
## 2683 married highskill tenant white male
## 2684 married highskill husband white male
## 2685 married highskill husband white male
## 2686 notmarried clerical single_occ black female
## 2687 divorce highskill tenant white male
## 2688 married highskill husband white male
## 2689 married midskill husband white male
## 2690 notmarried other relative white female
## 2691 married midskill husband white male
## 2692 married clerical husband white male
## 2693 married lowskill husband white male
## 2694 notmarried highskill tenant white male
## 2695 married midskill husband white male
## 2696 married midskill husband white male
## 2697 divorce lowskill depchild white male
## 2698 married highskill husband white male
## 2699 divorce agriculture tenant white male
## 2700 divorce highskill tenant white female
## 2701 divorce highskill depchild white male
## 2702 notmarried clerical tenant white female
## 2703 married highskill husband white male
## 2704 notmarried lowskill depchild white male
## 2705 notmarried lowskill depchild white female
## 2706 divorce highskill single_occ white female
## 2707 married lowskill relative white male
## 2708 married highskill husband white male
## 2709 divorce highskill tenant white female
## 2710 notmarried clerical depchild white female
## 2711 married highskill husband white male
## 2712 notmarried midskill relative white male
## 2713 notmarried lowskill tenant white male
## 2714 divorce highskill tenant white female
## 2715 notmarried midskill depchild white male
## 2716 widowed clerical tenant white female
## 2717 married highskill wife white female
## 2718 divorce highskill single_occ white male
## 2719 divorce highskill tenant native male
## 2720 married highskill husband white male
## 2721 notmarried clerical single_occ native female
## 2722 married highskill husband white male
## 2723 notmarried lowskill depchild white male
## 2724 notmarried highskill depchild white female
## 2725 divorce highskill single_occ white female
## 2726 notmarried highskill tenant white female
## 2727 notmarried highskill tenant white male
## 2728 notmarried clerical tenant white female
## 2729 notmarried lowskill depchild white female
## 2730 notmarried lowskill depchild white male
## 2731 married lowskill relative black female
## 2732 married highskill husband white male
## 2733 notmarried lowskill tenant white male
## 2734 divorce highskill single_occ white female
## 2735 married midskill husband white male
## 2736 notmarried highskill tenant white male
## 2737 married highskill husband white male
## 2738 divorce highskill relative white female
## 2739 married clerical wife white female
## 2740 notmarried highskill tenant white female
## 2741 married highskill husband white male
## 2742 married lowskill husband white male
## 2743 divorce lowskill single_occ black male
## 2744 married clerical husband white male
## 2745 married highskill husband white male
## 2746 married midskill husband black male
## 2747 divorce highskill relative white female
## 2748 married midskill husband white male
## 2749 married midskill husband white male
## 2750 notmarried midskill depchild white female
## 2751 divorce highskill tenant white male
## 2752 divorce other tenant white male
## 2753 married highskill husband white male
## 2754 married highskill husband white male
## 2755 married agriculture husband white male
## 2756 notmarried lowskill depchild white female
## 2757 married highskill husband white male
## 2758 notmarried lowskill depchild white male
## 2759 divorce highskill tenant black male
## 2760 married highskill husband white male
## 2761 married other husband white male
## 2762 divorce other depchild white male
## 2763 married midskill husband white male
## 2764 notmarried clerical tenant white female
## 2765 notmarried lowskill depchild white female
## 2766 divorce clerical single_occ white female
## 2767 notmarried midskill depchild white male
## 2768 married midskill husband white male
## 2769 divorce highskill tenant white female
## 2770 notmarried highskill depchild white male
## 2771 divorce clerical single_occ white female
## 2772 divorce highskill tenant white female
## 2773 notmarried lowskill depchild white male
## 2774 notmarried lowskill tenant black male
## 2775 married highskill husband white male
## 2776 married highskill husband black male
## 2777 notmarried highskill depchild white male
## 2778 divorce midskill tenant white male
## 2779 divorce clerical tenant white female
## 2780 divorce lowskill tenant white male
## 2781 married highskill husband white male
## 2782 married highskill husband white male
## 2783 notmarried highskill depchild black female
## 2784 divorce highskill tenant white male
## 2785 notmarried lowskill tenant black male
## 2786 widowed clerical single_occ white female
## 2787 married highskill husband white male
## 2788 notmarried highskill tenant white female
## 2789 notmarried highskill tenant white female
## 2790 divorce midskill tenant white male
## 2791 widowed clerical tenant white female
## 2792 divorce clerical tenant white female
## 2793 divorce midskill tenant white male
## 2794 widowed highskill tenant white female
## 2795 married highskill husband white male
## 2796 divorce midskill single_occ black female
## 2797 married highskill husband white male
## 2798 notmarried clerical tenant white male
## 2799 notmarried lowskill single_occ native male
## 2800 married highskill husband white male
## 2801 married highskill husband white male
## 2802 notmarried lowskill depchild black female
## 2803 married highskill husband white male
## 2804 notmarried highskill depchild white male
## 2805 married clerical wife white female
## 2806 notmarried highskill depchild white male
## 2807 divorce highskill tenant white male
## 2808 widowed lowskill single_occ black female
## 2809 divorce lowskill single_occ white female
## 2810 notmarried clerical tenant white female
## 2811 married agriculture husband white male
## 2812 widowed lowskill single_occ black female
## 2813 married highskill wife white female
## 2814 notmarried clerical tenant white male
## 2815 notmarried highskill depchild white male
## 2816 married midskill husband white male
## 2817 married midskill husband white male
## 2818 married midskill husband white male
## 2819 notmarried highskill tenant white female
## 2820 notmarried lowskill tenant white female
## 2821 married highskill husband white male
## 2822 notmarried midskill depchild white male
## 2823 divorce midskill depchild white male
## 2824 notmarried highskill depchild white male
## 2825 married highskill husband black male
## 2826 married highskill wife white female
## 2827 married midskill husband white male
## 2828 notmarried midskill tenant white male
## 2829 married highskill husband white male
## 2830 married highskill husband white male
## 2831 married highskill husband white male
## 2832 married highskill husband white male
## 2833 married highskill husband white male
## 2834 married highskill husband white male
## 2835 notmarried clerical tenant Asian_Pac_Islander female
## 2836 notmarried highskill depchild white female
## 2837 divorce midskill tenant white male
## 2838 married lowskill wife white female
## 2839 divorce highskill tenant white female
## 2840 married midskill husband white male
## 2841 notmarried highskill tenant white male
## 2842 divorce highskill single_occ black female
## 2843 married clerical husband black male
## 2844 married lowskill husband white male
## 2845 married highskill husband white male
## 2846 married highskill husband white male
## 2847 married highskill husband white male
## 2848 notmarried other single_occ black female
## 2849 married highskill husband white male
## 2850 notmarried lowskill depchild white female
## 2851 married agriculture husband white male
## 2852 notmarried highskill depchild white female
## 2853 married highskill wife white female
## 2854 divorce highskill relative white female
## 2855 notmarried highskill tenant white male
## 2856 notmarried lowskill single_occ black female
## 2857 married other husband white male
## 2858 divorce other single_occ white female
## 2859 notmarried other depchild white male
## 2860 notmarried highskill tenant white male
## 2861 married highskill husband white male
## 2862 notmarried midskill tenant white female
## 2863 notmarried lowskill single_occ white male
## 2864 married highskill husband white male
## 2865 married highskill wife white female
## 2866 divorce midskill single_occ white male
## 2867 married lowskill husband white male
## 2868 divorce lowskill single_occ white female
## 2869 notmarried highskill depchild white male
## 2870 notmarried lowskill depchild black male
## 2871 married midskill husband white male
## 2872 married highskill husband white male
## 2873 married midskill husband white male
## 2874 divorce highskill single_occ white female
## 2875 married clerical wife white female
## 2876 notmarried highskill relative black male
## 2877 notmarried midskill tenant white male
## 2878 married midskill husband white male
## 2879 married lowskill husband white male
## 2880 notmarried lowskill relative white male
## 2881 notmarried lowskill relative white male
## 2882 divorce clerical tenant white female
## 2883 married highskill husband black male
## 2884 divorce highskill tenant white female
## 2885 widowed midskill single_occ black male
## 2886 widowed other single_occ white female
## 2887 married midskill husband white male
## 2888 married highskill wife white female
## 2889 divorce highskill single_occ white female
## 2890 married clerical husband white male
## 2891 married lowskill depchild white female
## 2892 divorce clerical depchild white female
## 2893 married highskill husband white male
## 2894 notmarried highskill tenant white female
## 2895 married highskill husband white male
## 2896 divorce clerical single_occ white female
## 2897 married midskill husband white male
## 2898 divorce lowskill tenant white female
## 2899 notmarried highskill depchild Asian_Pac_Islander male
## 2900 divorce highskill single_occ white female
## 2901 married midskill husband white male
## 2902 married highskill husband white male
## 2903 married lowskill husband white male
## 2904 married highskill husband white male
## 2905 married lowskill husband white male
## 2906 divorce lowskill single_occ white female
## 2907 widowed lowskill tenant black female
## 2908 divorce clerical depchild white female
## 2909 notmarried lowskill depchild white female
## 2910 married highskill husband white male
## 2911 notmarried lowskill depchild white male
## 2912 notmarried highskill depchild white female
## 2913 divorce clerical single_occ white female
## 2914 notmarried clerical tenant white female
## 2915 notmarried midskill single_occ white female
## 2916 married highskill wife white female
## 2917 divorce highskill single_occ white female
## 2918 married highskill husband white male
## 2919 married highskill husband white male
## 2920 married highskill husband white male
## 2921 married agriculture husband white male
## 2922 notmarried midskill relative white male
## 2923 divorce midskill single_occ black male
## 2924 married highskill husband white male
## 2925 notmarried midskill tenant white male
## 2926 married highskill wife white female
## 2927 married midskill tenant Asian_Pac_Islander male
## 2928 divorce midskill depchild black female
## 2929 notmarried clerical depchild white female
## 2930 married midskill husband white male
## 2931 notmarried other depchild white female
## 2932 notmarried midskill tenant white male
## 2933 notmarried other single_occ white female
## 2934 notmarried highskill depchild white male
## 2935 notmarried midskill depchild white male
## 2936 married clerical husband white male
## 2937 notmarried lowskill depchild black male
## 2938 notmarried lowskill depchild white male
## 2939 married midskill wife black female
## 2940 married agriculture husband white male
## 2941 notmarried highskill single_occ black female
## 2942 married highskill husband white male
## 2943 married midskill husband white male
## 2944 notmarried highskill depchild Asian_Pac_Islander male
## 2945 notmarried lowskill depchild white female
## 2946 notmarried midskill depchild black male
## 2947 notmarried lowskill relative white female
## 2948 notmarried other depchild white male
## 2949 married highskill husband white male
## 2950 notmarried midskill depchild white male
## 2951 married highskill husband white male
## 2952 widowed highskill single_occ black female
## 2953 married other wife white female
## 2954 married highskill husband black male
## 2955 notmarried midskill single_occ black female
## 2956 married highskill husband white male
## 2957 married highskill husband white male
## 2958 divorce highskill single_occ white female
## 2959 divorce highskill tenant white female
## 2960 married agriculture husband white male
## 2961 notmarried highskill tenant white male
## 2962 widowed other tenant black male
## 2963 married highskill husband white male
## 2964 notmarried agriculture depchild white male
## 2965 notmarried midskill depchild white female
## 2966 married highskill husband white male
## 2967 notmarried midskill tenant white male
## 2968 widowed clerical tenant white female
## 2969 married highskill husband white male
## 2970 notmarried highskill tenant white male
## 2971 notmarried agriculture tenant white male
## 2972 married highskill husband white male
## 2973 divorce lowskill depchild white male
## 2974 married highskill husband white male
## 2975 married midskill husband black male
## 2976 notmarried clerical single_occ black female
## 2977 divorce midskill tenant white male
## 2978 married highskill husband white male
## 2979 divorce midskill single_occ white male
## 2980 divorce clerical single_occ black female
## 2981 notmarried highskill tenant white male
## 2982 notmarried lowskill relative white male
## 2983 married highskill husband white male
## 2984 married agriculture husband white male
## 2985 divorce clerical single_occ white female
## 2986 notmarried lowskill depchild white female
## 2987 married midskill husband black male
## 2988 notmarried lowskill tenant white male
## 2989 notmarried midskill depchild white male
## 2990 married highskill husband Asian_Pac_Islander male
## 2991 notmarried highskill depchild white male
## 2992 divorce highskill tenant white male
## 2993 notmarried highskill single_occ white female
## 2994 married agriculture wife native female
## 2995 married highskill husband white male
## 2996 married midskill husband white male
## 2997 notmarried highskill tenant white male
## 2998 divorce midskill relative white female
## 2999 married lowskill husband white male
## 3000 married other husband white male
## 3001 married midskill husband white male
## 3002 married midskill husband white male
## 3003 divorce lowskill single_occ white female
## 3004 divorce highskill tenant white female
## 3005 notmarried clerical tenant white female
## 3006 notmarried other depchild white male
## 3007 divorce highskill single_occ white female
## 3008 divorce highskill relative white male
## 3009 notmarried midskill relative white male
## 3010 married midskill husband white male
## 3011 married highskill wife white female
## 3012 divorce highskill tenant white male
## 3013 married highskill husband white male
## 3014 notmarried highskill tenant white female
## 3015 married lowskill husband white male
## 3016 notmarried highskill tenant white male
## 3017 married clerical husband white male
## 3018 married highskill husband white male
## 3019 notmarried agriculture depchild white male
## 3020 notmarried highskill tenant black female
## 3021 married highskill husband white male
## 3022 married midskill husband Asian_Pac_Islander male
## 3023 married midskill husband white male
## 3024 notmarried midskill tenant black male
## 3025 notmarried highskill depchild white female
## 3026 married midskill husband white male
## 3027 married midskill husband white male
## 3028 married highskill husband white male
## 3029 notmarried lowskill tenant white male
## 3030 married highskill husband white male
## 3031 widowed midskill single_occ white female
## 3032 divorce midskill single_occ white female
## 3033 married midskill husband white male
## 3034 divorce other tenant white male
## 3035 married clerical husband white male
## 3036 married highskill husband white male
## 3037 widowed highskill single_occ white female
## 3038 divorce clerical depchild white female
## 3039 married midskill husband white male
## 3040 married highskill husband white male
## 3041 notmarried highskill tenant black female
## 3042 notmarried highskill tenant white male
## 3043 married other husband white male
## 3044 notmarried clerical relative white female
## 3045 notmarried lowskill relative white male
## 3046 divorce midskill tenant white male
## 3047 divorce clerical tenant white male
## 3048 notmarried highskill tenant white male
## 3049 married agriculture husband white male
## 3050 married highskill husband white male
## 3051 married midskill husband white male
## 3052 divorce highskill single_occ white female
## 3053 married midskill husband white male
## 3054 married lowskill husband white male
## 3055 married highskill husband white male
## 3056 married midskill husband white male
## 3057 married highskill husband white male
## 3058 married highskill husband white male
## 3059 notmarried clerical depchild white female
## 3060 married midskill husband white male
## 3061 notmarried lowskill depchild white male
## 3062 divorce highskill tenant white female
## 3063 married midskill husband white male
## 3064 divorce lowskill tenant white male
## 3065 married highskill tenant Asian_Pac_Islander male
## 3066 widowed other tenant white male
## 3067 notmarried midskill depchild white male
## 3068 widowed clerical tenant white female
## 3069 divorce highskill tenant white male
## 3070 divorce lowskill single_occ black female
## 3071 notmarried highskill depchild white female
## 3072 married highskill husband white male
## 3073 notmarried other depchild black male
## 3074 notmarried midskill depchild black male
## 3075 notmarried highskill depchild white male
## 3076 notmarried highskill depchild white female
## 3077 married highskill husband white male
## 3078 married highskill husband white male
## 3079 notmarried lowskill depchild white male
## 3080 married highskill husband white male
## 3081 married highskill husband white male
## 3082 divorce midskill single_occ white female
## 3083 widowed highskill single_occ white male
## 3084 notmarried midskill depchild white male
## 3085 notmarried lowskill depchild white male
## 3086 notmarried agriculture depchild white male
## 3087 divorce midskill single_occ black female
## 3088 divorce clerical single_occ white female
## 3089 notmarried other depchild white male
## 3090 married highskill husband white male
## 3091 divorce clerical tenant white female
## 3092 married highskill husband white male
## 3093 married agriculture husband white male
## 3094 married highskill husband white male
## 3095 married highskill wife white female
## 3096 married highskill husband white male
## 3097 notmarried other single_occ black female
## 3098 notmarried highskill tenant white female
## 3099 divorce highskill single_occ white female
## 3100 notmarried highskill depchild white female
## 3101 married midskill husband white male
## 3102 married agriculture husband white male
## 3103 divorce midskill depchild white female
## 3104 notmarried lowskill depchild white female
## 3105 married highskill husband white male
## 3106 married highskill husband white male
## 3107 married highskill husband white male
## 3108 married highskill husband white male
## 3109 married highskill depchild white male
## 3110 notmarried highskill depchild white female
## 3111 notmarried highskill tenant Asian_Pac_Islander male
## 3112 married highskill husband white male
## 3113 notmarried clerical depchild white male
## 3114 married highskill husband white male
## 3115 notmarried lowskill depchild white male
## 3116 married highskill husband white male
## 3117 married highskill husband white male
## 3118 notmarried agriculture tenant white male
## 3119 notmarried agriculture tenant white male
## 3120 married other husband white male
## 3121 divorce clerical single_occ white female
## 3122 notmarried midskill depchild white male
## 3123 married clerical wife white female
## 3124 divorce clerical single_occ white female
## 3125 notmarried clerical depchild white female
## 3126 married highskill husband white male
## 3127 married clerical husband white male
## 3128 notmarried highskill depchild white male
## 3129 married midskill husband white male
## 3130 divorce highskill tenant white female
## 3131 divorce lowskill single_occ other female
## 3132 notmarried other depchild black male
## 3133 married midskill husband white male
## 3134 notmarried midskill depchild white male
## 3135 married highskill husband white male
## 3136 married midskill husband white male
## 3137 notmarried agriculture tenant white male
## 3138 married highskill husband white male
## 3139 divorce clerical single_occ white female
## 3140 notmarried clerical depchild white male
## 3141 married midskill husband white male
## 3142 married midskill husband white male
## 3143 married midskill husband white male
## 3144 married highskill husband black male
## 3145 married highskill husband white male
## 3146 married highskill husband white male
## 3147 divorce other tenant white female
## 3148 notmarried highskill tenant white female
## 3149 married highskill husband Asian_Pac_Islander male
## 3150 married highskill husband white male
## 3151 married lowskill husband white male
## 3152 notmarried highskill tenant white male
## 3153 married highskill husband white male
## 3154 married highskill husband white male
## 3155 married highskill husband white male
## 3156 married highskill husband white male
## 3157 notmarried highskill depchild white male
## 3158 married lowskill husband white male
## 3159 widowed midskill tenant black male
## 3160 married highskill husband white male
## 3161 divorce highskill single_occ white male
## 3162 notmarried midskill depchild black male
## 3163 notmarried midskill tenant white male
## 3164 married highskill wife white female
## 3165 married clerical husband white male
## 3166 married highskill wife white female
## 3167 married midskill husband white male
## 3168 married highskill husband white male
## 3169 married highskill husband white male
## 3170 married midskill husband white male
## 3171 notmarried clerical depchild white female
## 3172 married highskill husband white male
## 3173 notmarried highskill tenant white male
## 3174 married lowskill wife white female
## 3175 married highskill husband white male
## 3176 divorce highskill tenant white male
## 3177 married clerical wife white female
## 3178 notmarried lowskill depchild white male
## 3179 married lowskill single_occ white female
## 3180 notmarried lowskill tenant black female
## 3181 married highskill husband white male
## 3182 notmarried midskill depchild white male
## 3183 notmarried midskill single_occ white male
## 3184 widowed highskill tenant white female
## 3185 notmarried highskill single_occ Asian_Pac_Islander female
## 3186 married highskill husband white male
## 3187 married highskill husband white male
## 3188 married lowskill tenant white female
## 3189 divorce highskill single_occ black female
## 3190 married midskill husband white male
## 3191 married midskill husband white male
## 3192 divorce midskill tenant black female
## 3193 notmarried lowskill tenant white female
## 3194 divorce midskill single_occ black female
## 3195 divorce lowskill depchild black male
## 3196 married highskill single_occ white male
## 3197 married agriculture relative white male
## 3198 married highskill husband white male
## 3199 married midskill wife white female
## 3200 married highskill husband Asian_Pac_Islander male
## 3201 notmarried highskill depchild white male
## 3202 notmarried midskill tenant white male
## 3203 notmarried highskill depchild Asian_Pac_Islander male
## 3204 married highskill wife white female
## 3205 widowed highskill single_occ white male
## 3206 notmarried midskill single_occ white male
## 3207 married highskill husband white male
## 3208 notmarried other depchild other male
## 3209 widowed midskill tenant white male
## 3210 widowed highskill tenant white female
## 3211 married lowskill husband white male
## 3212 widowed other tenant white male
## 3213 married highskill husband white male
## 3214 widowed lowskill tenant white male
## 3215 notmarried clerical depchild white male
## 3216 married midskill husband white male
## 3217 married agriculture husband white male
## 3218 married midskill husband white male
## 3219 married highskill husband white male
## 3220 divorce clerical single_occ white female
## 3221 divorce highskill tenant white male
## 3222 married lowskill husband white male
## 3223 notmarried midskill single_occ white male
## 3224 divorce clerical tenant black male
## 3225 married highskill wife white female
## 3226 married agriculture husband white male
## 3227 married midskill husband white male
## 3228 married highskill husband white male
## 3229 notmarried other depchild black male
## 3230 divorce midskill tenant white male
## 3231 married highskill wife black female
## 3232 married other husband white male
## 3233 divorce highskill tenant white male
## 3234 divorce midskill tenant white female
## 3235 notmarried lowskill single_occ black female
## 3236 married midskill husband white male
## 3237 married midskill tenant white male
## 3238 notmarried highskill depchild white male
## 3239 married highskill husband other male
## 3240 notmarried other depchild Asian_Pac_Islander female
## 3241 notmarried lowskill single_occ white female
## 3242 divorce lowskill tenant black male
## 3243 notmarried lowskill tenant white male
## 3244 notmarried midskill relative white male
## 3245 divorce highskill single_occ white male
## 3246 married highskill husband white male
## 3247 notmarried highskill tenant white male
## 3248 married clerical wife white female
## 3249 married highskill husband white male
## 3250 married highskill husband white male
## 3251 married clerical husband white male
## 3252 notmarried highskill depchild black female
## 3253 notmarried highskill tenant white female
## 3254 notmarried lowskill single_occ white male
## 3255 married midskill husband white male
## 3256 divorce other tenant white male
## 3257 married highskill husband white male
## 3258 divorce midskill depchild white male
## 3259 notmarried lowskill depchild white male
## 3260 divorce highskill single_occ white female
## 3261 married highskill husband white male
## 3262 notmarried midskill tenant white male
## 3263 married midskill husband white male
## 3264 notmarried clerical tenant white female
## 3265 divorce highskill tenant white male
## 3266 married highskill husband white male
## 3267 married highskill husband white male
## 3268 married highskill husband white male
## 3269 notmarried highskill tenant black male
## 3270 married other wife black female
## 3271 widowed lowskill tenant white female
## 3272 married highskill husband white male
## 3273 notmarried midskill tenant white male
## 3274 married midskill husband white male
## 3275 married highskill husband white male
## 3276 divorce clerical single_occ white female
## 3277 notmarried highskill tenant white female
## 3278 married midskill husband white male
## 3279 married midskill husband white male
## 3280 divorce highskill single_occ white female
## 3281 notmarried midskill tenant white male
## 3282 notmarried highskill tenant white male
## 3283 notmarried agriculture relative white male
## 3284 divorce lowskill relative white female
## 3285 married clerical husband white male
## 3286 notmarried lowskill depchild white male
## 3287 notmarried lowskill tenant white male
## 3288 married agriculture husband white male
## 3289 married midskill husband white male
## 3290 married midskill husband white male
## 3291 divorce highskill single_occ white female
## 3292 married other husband white male
## 3293 married highskill husband white male
## 3294 married highskill husband white male
## 3295 divorce highskill single_occ white female
## 3296 notmarried highskill tenant white female
## 3297 married midskill husband white male
## 3298 married other husband white male
## 3299 notmarried lowskill single_occ black female
## 3300 notmarried highskill tenant white female
## 3301 divorce clerical single_occ white female
## 3302 divorce midskill relative white female
## 3303 married clerical husband white male
## 3304 married lowskill husband white male
## 3305 divorce clerical single_occ white female
## 3306 divorce highskill tenant white male
## 3307 notmarried highskill tenant white male
## 3308 divorce clerical tenant white female
## 3309 married lowskill husband black male
## 3310 married clerical husband white male
## 3311 notmarried highskill tenant white female
## 3312 married highskill husband white male
## 3313 married agriculture husband white male
## 3314 notmarried highskill depchild white female
## 3315 notmarried midskill tenant black male
## 3316 widowed highskill tenant white female
## 3317 notmarried highskill tenant black male
## 3318 married highskill husband white male
## 3319 married highskill husband white male
## 3320 notmarried midskill depchild white male
## 3321 notmarried clerical depchild white female
## 3322 married highskill husband white male
## 3323 married midskill husband white male
## 3324 married midskill husband white male
## 3325 widowed highskill tenant white female
## 3326 divorce clerical single_occ white female
## 3327 divorce agriculture tenant white female
## 3328 married highskill husband white male
## 3329 married highskill husband white male
## 3330 divorce clerical single_occ white female
## 3331 notmarried other tenant white male
## 3332 notmarried highskill relative white male
## 3333 divorce clerical single_occ white female
## 3334 married midskill husband white male
## 3335 notmarried lowskill depchild black male
## 3336 divorce midskill tenant white female
## 3337 married clerical wife white female
## 3338 married highskill husband white male
## 3339 widowed other tenant black female
## 3340 widowed midskill single_occ white female
## 3341 divorce lowskill tenant white male
## 3342 divorce clerical single_occ white female
## 3343 divorce lowskill single_occ white female
## 3344 married highskill husband other male
## 3345 notmarried clerical depchild white male
## 3346 notmarried midskill tenant black female
## 3347 divorce clerical single_occ white female
## 3348 married midskill relative white male
## 3349 married midskill husband white male
## 3350 married highskill wife white female
## 3351 married agriculture tenant other male
## 3352 notmarried other tenant white female
## 3353 married clerical wife white female
## 3354 married midskill husband white male
## 3355 notmarried lowskill single_occ white male
## 3356 notmarried highskill tenant white male
## 3357 notmarried clerical depchild white female
## 3358 notmarried clerical depchild white female
## 3359 married clerical husband white male
## 3360 notmarried highskill tenant white male
## 3361 married highskill wife white female
## 3362 notmarried lowskill relative white female
## 3363 notmarried lowskill depchild black female
## 3364 married highskill husband white male
## 3365 notmarried clerical depchild white female
## 3366 married highskill husband white male
## 3367 married midskill husband white male
## 3368 married highskill husband white male
## 3369 married highskill husband white male
## 3370 married highskill husband white male
## 3371 married highskill husband white male
## 3372 notmarried other depchild white male
## 3373 divorce lowskill depchild white female
## 3374 divorce highskill single_occ white female
## 3375 married midskill husband white male
## 3376 married midskill husband white male
## 3377 married midskill husband black male
## 3378 married highskill husband white male
## 3379 married midskill husband white male
## 3380 divorce clerical single_occ white female
## 3381 notmarried midskill single_occ other male
## 3382 married highskill husband white male
## 3383 married midskill husband white male
## 3384 married midskill husband white male
## 3385 married midskill wife black female
## 3386 married midskill husband white male
## 3387 married highskill husband white male
## 3388 married other tenant white male
## 3389 married agriculture husband white male
## 3390 married highskill husband white male
## 3391 widowed highskill single_occ white male
## 3392 notmarried lowskill depchild white female
## 3393 married highskill husband white male
## 3394 married agriculture husband white male
## 3395 notmarried lowskill depchild white female
## 3396 divorce highskill tenant white male
## 3397 notmarried highskill tenant white female
## 3398 notmarried midskill depchild black male
## 3399 married highskill husband white male
## 3400 married midskill husband black male
## 3401 married midskill husband white male
## 3402 married midskill husband white male
## 3403 married other husband black male
## 3404 notmarried agriculture tenant white male
## 3405 married clerical husband white male
## 3406 married highskill husband white male
## 3407 married midskill husband native male
## 3408 married highskill husband white male
## 3409 married highskill husband white male
## 3410 divorce lowskill single_occ black female
## 3411 married highskill husband white male
## 3412 notmarried midskill tenant black female
## 3413 married midskill husband white male
## 3414 married highskill husband white male
## 3415 married midskill husband white male
## 3416 married midskill husband white male
## 3417 divorce highskill single_occ white male
## 3418 notmarried highskill tenant white female
## 3419 married highskill husband white male
## 3420 notmarried highskill tenant white male
## 3421 notmarried lowskill tenant white male
## 3422 divorce clerical tenant white female
## 3423 divorce lowskill tenant white female
## 3424 married highskill tenant white male
## 3425 married agriculture husband white male
## 3426 notmarried lowskill depchild white male
## 3427 divorce highskill tenant white male
## 3428 married highskill husband white male
## 3429 married highskill husband white male
## 3430 notmarried midskill depchild white female
## 3431 married highskill wife white female
## 3432 notmarried highskill depchild white female
## 3433 married midskill husband white male
## 3434 married highskill husband white male
## 3435 divorce lowskill tenant white female
## 3436 married midskill husband white male
## 3437 married midskill husband white male
## 3438 married midskill husband white male
## 3439 married midskill husband white male
## 3440 divorce other tenant white female
## 3441 notmarried highskill tenant white male
## 3442 notmarried lowskill tenant white male
## 3443 married midskill husband black male
## 3444 notmarried lowskill relative native male
## 3445 divorce highskill tenant white male
## 3446 married highskill husband white male
## 3447 married agriculture husband white male
## 3448 married midskill husband white male
## 3449 married highskill husband white male
## 3450 notmarried highskill tenant white female
## 3451 divorce highskill depchild white male
## 3452 married highskill wife white female
## 3453 notmarried clerical depchild white female
## 3454 notmarried other relative other female
## 3455 divorce highskill single_occ black female
## 3456 notmarried midskill depchild white male
## 3457 notmarried other depchild white female
## 3458 notmarried highskill depchild white female
## 3459 married highskill husband white male
## 3460 notmarried other single_occ white female
## 3461 married midskill husband white male
## 3462 widowed lowskill single_occ white female
## 3463 divorce midskill tenant white male
## 3464 notmarried highskill depchild white male
## 3465 married clerical husband white male
## 3466 notmarried highskill tenant white male
## 3467 married highskill husband white male
## 3468 married midskill husband white male
## 3469 married highskill wife white female
## 3470 married highskill husband white male
## 3471 married highskill husband white male
## 3472 married midskill husband white male
## 3473 notmarried highskill depchild white female
## 3474 notmarried lowskill depchild white male
## 3475 divorce highskill single_occ white female
## 3476 married highskill husband white male
## 3477 notmarried highskill depchild white male
## 3478 married midskill husband black male
## 3479 notmarried highskill single_occ white female
## 3480 notmarried highskill tenant white female
## 3481 married highskill husband white male
## 3482 notmarried lowskill tenant white female
## 3483 divorce highskill single_occ white female
## 3484 married highskill husband white male
## 3485 notmarried midskill depchild black male
## 3486 married highskill husband white male
## 3487 notmarried other tenant black female
## 3488 notmarried midskill tenant white female
## 3489 notmarried highskill tenant white female
## 3490 married highskill wife white female
## 3491 married lowskill husband white male
## 3492 notmarried clerical single_occ white female
## 3493 married highskill husband white male
## 3494 married lowskill wife white female
## 3495 notmarried highskill tenant white female
## 3496 notmarried lowskill depchild black male
## 3497 notmarried lowskill relative black male
## 3498 married midskill husband white male
## 3499 married lowskill husband white male
## 3500 married highskill husband white male
## 3501 married highskill husband white male
## 3502 notmarried highskill single_occ white female
## 3503 married midskill husband white male
## 3504 divorce highskill single_occ white female
## 3505 notmarried highskill tenant white female
## 3506 married highskill husband white male
## 3507 divorce clerical depchild white male
## 3508 notmarried highskill tenant white male
## 3509 married midskill husband white male
## 3510 notmarried highskill depchild white female
## 3511 widowed highskill tenant white female
## 3512 divorce midskill single_occ white female
## 3513 notmarried clerical depchild white female
## 3514 notmarried highskill tenant white male
## 3515 divorce highskill single_occ white female
## 3516 divorce highskill tenant white female
## 3517 notmarried other depchild white female
## 3518 married midskill husband white male
## 3519 married highskill husband white male
## 3520 divorce lowskill single_occ white female
## 3521 married midskill husband white male
## 3522 married midskill husband white male
## 3523 married midskill husband white male
## 3524 married highskill husband white male
## 3525 notmarried lowskill depchild white male
## 3526 married midskill husband white male
## 3527 notmarried clerical depchild black male
## 3528 married lowskill husband white male
## 3529 notmarried lowskill depchild white male
## 3530 divorce midskill tenant black female
## 3531 married highskill husband white male
## 3532 divorce other single_occ white female
## 3533 notmarried agriculture tenant white male
## 3534 married midskill husband white male
## 3535 notmarried lowskill depchild white male
## 3536 notmarried lowskill relative black male
## 3537 married highskill husband white male
## 3538 widowed clerical tenant white female
## 3539 married highskill husband white male
## 3540 married midskill husband white male
## 3541 married highskill husband white male
## 3542 married agriculture husband white male
## 3543 notmarried highskill tenant white female
## 3544 married clerical wife white female
## 3545 divorce clerical single_occ white female
## 3546 divorce highskill tenant white female
## 3547 notmarried lowskill depchild black female
## 3548 married midskill husband white male
## 3549 notmarried highskill single_occ black female
## 3550 notmarried highskill depchild white female
## 3551 married highskill husband white male
## 3552 notmarried highskill tenant black male
## 3553 married midskill husband white male
## 3554 divorce highskill tenant white female
## 3555 notmarried highskill single_occ white female
## 3556 notmarried other tenant other female
## 3557 divorce highskill tenant white female
## 3558 notmarried clerical tenant Asian_Pac_Islander male
## 3559 notmarried highskill depchild white female
## 3560 divorce highskill tenant black male
## 3561 divorce lowskill tenant white female
## 3562 married agriculture husband white male
## 3563 notmarried midskill tenant white male
## 3564 divorce midskill tenant white male
## 3565 married highskill husband white male
## 3566 divorce lowskill single_occ white female
## 3567 notmarried highskill tenant white male
## 3568 married midskill husband white male
## 3569 married midskill husband white male
## 3570 notmarried highskill tenant white female
## 3571 married midskill husband white male
## 3572 married midskill husband white male
## 3573 notmarried other depchild white female
## 3574 notmarried other tenant white male
## 3575 married highskill husband white male
## 3576 married highskill husband white male
## 3577 notmarried lowskill depchild white female
## 3578 married highskill husband white male
## 3579 married highskill husband black male
## 3580 notmarried other tenant Asian_Pac_Islander male
## 3581 married midskill husband white male
## 3582 married midskill single_occ white male
## 3583 married lowskill husband white male
## 3584 married highskill husband white male
## 3585 notmarried highskill tenant white female
## 3586 notmarried lowskill relative white male
## 3587 notmarried highskill tenant white female
## 3588 divorce highskill single_occ white female
## 3589 widowed other tenant white female
## 3590 notmarried highskill depchild native female
## 3591 notmarried highskill tenant white male
## 3592 notmarried highskill depchild white female
## 3593 widowed other tenant white female
## 3594 married agriculture wife white female
## 3595 married other wife white female
## 3596 married lowskill wife white female
## 3597 notmarried clerical depchild black female
## 3598 notmarried other depchild white male
## 3599 divorce clerical tenant native female
## 3600 married highskill husband white male
## 3601 divorce lowskill tenant white female
## 3602 notmarried highskill tenant white male
## 3603 married highskill husband white male
## 3604 married midskill husband white male
## 3605 married highskill husband white male
## 3606 notmarried lowskill depchild native female
## 3607 married midskill husband white male
## 3608 divorce midskill tenant white male
## 3609 notmarried midskill depchild black female
## 3610 notmarried highskill tenant white female
## 3611 married clerical husband white male
## 3612 married highskill wife white female
## 3613 married highskill husband black male
## 3614 notmarried clerical depchild black female
## 3615 married midskill husband white male
## 3616 divorce highskill depchild white female
## 3617 notmarried highskill tenant white male
## 3618 divorce agriculture single_occ white male
## 3619 notmarried lowskill depchild white male
## 3620 married highskill husband white male
## 3621 divorce clerical relative white female
## 3622 notmarried highskill tenant white male
## 3623 notmarried lowskill tenant white female
## 3624 married midskill husband white male
## 3625 widowed clerical tenant white female
## 3626 married highskill husband white male
## 3627 married clerical wife white female
## 3628 married highskill husband Asian_Pac_Islander male
## 3629 notmarried highskill relative white female
## 3630 married highskill husband white male
## 3631 divorce highskill single_occ white female
## 3632 married other husband white male
## 3633 notmarried clerical depchild white female
## 3634 widowed highskill tenant white female
## 3635 divorce midskill tenant white male
## 3636 married midskill husband other male
## 3637 married midskill husband Asian_Pac_Islander male
## 3638 divorce clerical single_occ white female
## 3639 notmarried midskill tenant white male
## 3640 married highskill husband white male
## 3641 notmarried midskill tenant white male
## 3642 notmarried midskill single_occ white female
## 3643 married midskill husband white male
## 3644 married midskill husband white male
## 3645 married midskill husband white male
## 3646 notmarried lowskill depchild white female
## 3647 married highskill wife white female
## 3648 married midskill wife Asian_Pac_Islander female
## 3649 notmarried highskill tenant white female
## 3650 notmarried lowskill depchild white male
## 3651 notmarried clerical depchild black male
## 3652 married highskill husband white male
## 3653 married highskill husband black male
## 3654 divorce midskill depchild white male
## 3655 married midskill husband white male
## 3656 married highskill husband white male
## 3657 married midskill husband white male
## 3658 married lowskill husband Asian_Pac_Islander male
## 3659 notmarried highskill depchild black female
## 3660 married lowskill husband white male
## 3661 married midskill husband white male
## 3662 notmarried clerical tenant white female
## 3663 notmarried agriculture tenant white male
## 3664 married midskill husband white male
## 3665 married highskill husband white male
## 3666 notmarried highskill tenant black male
## 3667 married highskill husband white male
## 3668 divorce highskill single_occ white female
## 3669 married midskill husband white male
## 3670 married midskill husband white male
## 3671 divorce other depchild white male
## 3672 married highskill husband white male
## 3673 married highskill husband other male
## 3674 married midskill husband white male
## 3675 married highskill husband white male
## 3676 notmarried lowskill relative white male
## 3677 married midskill husband native male
## 3678 divorce highskill tenant white female
## 3679 married clerical husband white male
## 3680 notmarried highskill depchild white female
## 3681 married highskill husband black male
## 3682 notmarried highskill depchild white male
## 3683 married highskill husband black male
## 3684 divorce clerical tenant white female
## 3685 married highskill husband white male
## 3686 married midskill husband white male
## 3687 notmarried lowskill relative white female
## 3688 notmarried lowskill relative white male
## 3689 divorce lowskill tenant white male
## 3690 married highskill husband white male
## 3691 married midskill husband white male
## 3692 married midskill husband white male
## 3693 married midskill husband white male
## 3694 married clerical wife white female
## 3695 married lowskill husband white male
## 3696 divorce highskill single_occ white female
## 3697 notmarried midskill relative white female
## 3698 married highskill husband white male
## 3699 married highskill husband white male
## 3700 divorce midskill tenant white male
## 3701 married highskill husband white male
## 3702 married lowskill husband white male
## 3703 notmarried midskill tenant black male
## 3704 married other husband white male
## 3705 divorce highskill tenant white female
## 3706 married midskill husband white male
## 3707 notmarried lowskill relative black male
## 3708 married midskill husband native male
## 3709 married clerical husband Asian_Pac_Islander male
## 3710 married highskill wife white female
## 3711 notmarried clerical tenant white male
## 3712 married agriculture husband white male
## 3713 notmarried clerical depchild white female
## 3714 divorce highskill depchild white male
## 3715 married highskill relative Asian_Pac_Islander female
## 3716 notmarried clerical depchild Asian_Pac_Islander male
## 3717 married highskill husband black male
## 3718 notmarried highskill depchild white male
## 3719 notmarried lowskill depchild white female
## 3720 notmarried clerical depchild white male
## 3721 married midskill husband white male
## 3722 married highskill husband white male
## 3723 notmarried clerical single_occ white male
## 3724 married highskill husband white male
## 3725 married highskill husband white male
## 3726 notmarried other depchild white male
## 3727 married clerical wife white female
## 3728 married highskill wife white female
## 3729 married midskill husband white male
## 3730 notmarried highskill single_occ native female
## 3731 notmarried clerical tenant white female
## 3732 married midskill husband white male
## 3733 married midskill husband white male
## 3734 married highskill wife white female
## 3735 notmarried highskill depchild white female
## 3736 married clerical husband white male
## 3737 married other wife white female
## 3738 notmarried lowskill relative black male
## 3739 married midskill husband white male
## 3740 married lowskill husband white male
## 3741 married lowskill husband white male
## 3742 married clerical husband white male
## 3743 divorce midskill tenant white male
## 3744 divorce highskill tenant other female
## 3745 notmarried other depchild white male
## 3746 married agriculture husband white male
## 3747 divorce lowskill single_occ black female
## 3748 divorce other single_occ white female
## 3749 married midskill husband white male
## 3750 married clerical husband white male
## 3751 divorce highskill tenant white female
## 3752 married highskill husband white male
## 3753 married midskill husband white male
## 3754 notmarried clerical tenant Asian_Pac_Islander female
## 3755 married highskill husband white male
## 3756 notmarried highskill relative black male
## 3757 divorce clerical single_occ white female
## 3758 married agriculture wife white female
## 3759 married midskill husband white male
## 3760 married other husband white male
## 3761 married midskill husband white male
## 3762 notmarried highskill tenant white female
## 3763 notmarried lowskill tenant black female
## 3764 notmarried clerical tenant white male
## 3765 notmarried lowskill depchild white male
## 3766 divorce highskill single_occ black female
## 3767 married highskill husband white male
## 3768 notmarried clerical single_occ black female
## 3769 married lowskill husband white male
## 3770 notmarried lowskill tenant white female
## 3771 notmarried highskill single_occ white female
## 3772 divorce lowskill depchild white female
## 3773 married midskill husband white male
## 3774 widowed other tenant white female
## 3775 married highskill husband white male
## 3776 notmarried other depchild white female
## 3777 divorce clerical tenant white female
## 3778 notmarried highskill tenant white male
## 3779 married highskill husband white male
## 3780 divorce midskill single_occ white male
## 3781 divorce clerical single_occ white female
## 3782 notmarried clerical tenant white female
## 3783 widowed midskill tenant white male
## 3784 married highskill husband white male
## 3785 married midskill husband white male
## 3786 notmarried midskill tenant white male
## 3787 married highskill husband white male
## 3788 married highskill husband white male
## 3789 married highskill husband white male
## 3790 married highskill husband white male
## 3791 divorce highskill tenant white male
## 3792 notmarried highskill depchild black male
## 3793 married clerical husband white male
## 3794 notmarried lowskill depchild white male
## 3795 notmarried midskill tenant white male
## 3796 married midskill husband white male
## 3797 notmarried midskill tenant white male
## 3798 married midskill husband white male
## 3799 notmarried highskill relative white male
## 3800 married midskill husband white male
## 3801 married highskill husband white male
## 3802 notmarried lowskill depchild white male
## 3803 notmarried clerical depchild white male
## 3804 notmarried lowskill depchild white male
## 3805 notmarried lowskill relative white male
## 3806 notmarried other depchild white female
## 3807 married highskill husband white male
## 3808 notmarried highskill depchild white female
## 3809 married highskill husband white male
## 3810 married highskill husband white male
## 3811 notmarried highskill tenant black female
## 3812 divorce highskill single_occ white female
## 3813 married highskill husband Asian_Pac_Islander male
## 3814 divorce highskill tenant white female
## 3815 notmarried highskill tenant white female
## 3816 notmarried lowskill depchild white female
## 3817 married highskill relative white male
## 3818 married highskill husband white male
## 3819 married midskill husband white male
## 3820 notmarried clerical tenant white female
## 3821 notmarried agriculture single_occ white male
## 3822 married lowskill wife native female
## 3823 divorce other tenant white female
## 3824 divorce midskill tenant white male
## 3825 notmarried agriculture depchild white male
## 3826 notmarried midskill depchild black male
## 3827 married agriculture husband white male
## 3828 married midskill husband white male
## 3829 notmarried midskill tenant white male
## 3830 married highskill husband white male
## 3831 married lowskill husband white male
## 3832 married highskill husband white male
## 3833 married midskill husband Asian_Pac_Islander male
## 3834 married highskill husband white male
## 3835 notmarried other tenant white female
## 3836 notmarried lowskill tenant white female
## 3837 married highskill husband white male
## 3838 divorce highskill tenant white male
## 3839 married highskill husband native male
## 3840 notmarried midskill tenant white male
## 3841 married clerical husband white male
## 3842 divorce midskill single_occ white female
## 3843 divorce clerical single_occ black female
## 3844 notmarried other depchild native male
## 3845 notmarried highskill depchild white male
## 3846 married clerical husband white male
## 3847 divorce clerical tenant white female
## 3848 married highskill wife white female
## 3849 divorce highskill single_occ white male
## 3850 notmarried highskill tenant white female
## 3851 married highskill husband white male
## 3852 notmarried other depchild white male
## 3853 notmarried lowskill depchild white female
## 3854 notmarried lowskill tenant white male
## 3855 married lowskill husband white male
## 3856 notmarried highskill tenant white female
## 3857 notmarried clerical tenant Asian_Pac_Islander male
## 3858 divorce highskill tenant white female
## 3859 notmarried highskill single_occ black female
## 3860 divorce highskill single_occ white female
## 3861 married highskill husband white male
## 3862 divorce midskill single_occ black male
## 3863 married highskill husband white male
## 3864 married other husband white male
## 3865 married midskill husband black male
## 3866 married highskill wife white female
## 3867 married midskill husband white male
## 3868 married lowskill husband white male
## 3869 notmarried midskill tenant white male
## 3870 notmarried highskill tenant white female
## 3871 married lowskill husband white male
## 3872 married highskill husband black male
## 3873 married midskill husband white male
## 3874 notmarried highskill tenant white female
## 3875 notmarried highskill depchild white male
## 3876 divorce lowskill tenant white male
## 3877 married midskill husband white male
## 3878 divorce highskill single_occ white male
## 3879 married highskill husband white male
## 3880 married midskill wife Asian_Pac_Islander female
## 3881 married highskill husband white male
## 3882 widowed highskill tenant white female
## 3883 married highskill husband white male
## 3884 married midskill husband white male
## 3885 married highskill husband white male
## 3886 divorce highskill depchild white female
## 3887 married highskill husband white male
## 3888 married other wife white female
## 3889 divorce midskill single_occ white male
## 3890 married midskill husband black male
## 3891 divorce clerical depchild white male
## 3892 notmarried midskill depchild white male
## 3893 married highskill husband white male
## 3894 divorce highskill tenant white male
## 3895 married clerical husband white male
## 3896 married other wife white female
## 3897 divorce highskill single_occ white female
## 3898 notmarried other depchild black male
## 3899 notmarried lowskill depchild white male
## 3900 notmarried midskill relative black male
## 3901 married highskill husband white male
## 3902 notmarried other depchild white male
## 3903 notmarried other depchild white male
## 3904 notmarried lowskill tenant white male
## 3905 married clerical husband white male
## 3906 married lowskill husband white male
## 3907 married highskill husband black male
## 3908 notmarried clerical tenant white male
## 3909 married lowskill husband black male
## 3910 married highskill husband Asian_Pac_Islander male
## 3911 divorce highskill single_occ white female
## 3912 married midskill husband white male
## 3913 married highskill husband white male
## 3914 married highskill husband white male
## 3915 married midskill husband white male
## 3916 married midskill husband white male
## 3917 notmarried other tenant white male
## 3918 notmarried lowskill depchild white male
## 3919 married highskill husband white male
## 3920 notmarried midskill depchild white male
## 3921 married highskill husband black male
## 3922 divorce midskill tenant white male
## 3923 notmarried clerical depchild white female
## 3924 married lowskill husband black male
## 3925 married clerical husband white male
## 3926 married highskill husband white male
## 3927 divorce highskill tenant white male
## 3928 married highskill husband white male
## 3929 married lowskill husband white male
## 3930 married midskill husband white male
## 3931 notmarried highskill depchild white female
## 3932 widowed lowskill single_occ black male
## 3933 married midskill husband white male
## 3934 married midskill husband white male
## 3935 divorce highskill tenant white female
## 3936 notmarried highskill depchild white female
## 3937 divorce clerical single_occ white female
## 3938 divorce highskill tenant white female
## 3939 divorce midskill single_occ white male
## 3940 married midskill husband white male
## 3941 notmarried highskill tenant black female
## 3942 married midskill husband white male
## 3943 notmarried other depchild white male
## 3944 notmarried clerical single_occ white female
## 3945 married midskill husband white male
## 3946 married highskill husband white male
## 3947 notmarried highskill tenant white male
## 3948 divorce other tenant black female
## 3949 married highskill husband white male
## 3950 divorce other single_occ white female
## 3951 divorce other single_occ white female
## 3952 notmarried highskill tenant white male
## 3953 married highskill husband white male
## 3954 married midskill husband black male
## 3955 married highskill husband white male
## 3956 notmarried midskill relative white female
## 3957 divorce highskill tenant white female
## 3958 divorce highskill tenant white female
## 3959 married midskill husband black male
## 3960 married midskill husband white male
## 3961 divorce highskill single_occ white male
## 3962 married midskill husband white male
## 3963 married midskill husband white male
## 3964 widowed other tenant white female
## 3965 married highskill husband white male
## 3966 married midskill wife white female
## 3967 married highskill wife white female
## 3968 married midskill husband white male
## 3969 married midskill husband white male
## 3970 divorce other tenant white male
## 3971 notmarried clerical tenant white male
## 3972 divorce lowskill tenant white male
## 3973 notmarried lowskill depchild white male
## 3974 notmarried clerical depchild black female
## 3975 divorce clerical tenant white female
## 3976 notmarried highskill tenant white female
## 3977 married agriculture husband white male
## 3978 notmarried clerical depchild white male
## 3979 married highskill husband white male
## 3980 divorce highskill tenant white male
## 3981 married other husband black male
## 3982 notmarried midskill tenant white male
## 3983 divorce highskill single_occ white female
## 3984 married highskill husband white male
## 3985 divorce lowskill tenant black male
## 3986 notmarried lowskill depchild black male
## 3987 divorce clerical tenant white female
## 3988 married lowskill husband Asian_Pac_Islander male
## 3989 married midskill husband white male
## 3990 notmarried highskill depchild white male
## 3991 widowed other tenant white female
## 3992 notmarried clerical depchild white male
## 3993 divorce midskill relative white male
## 3994 married highskill depchild Asian_Pac_Islander female
## 3995 divorce highskill tenant white female
## 3996 notmarried clerical single_occ white female
## 3997 notmarried lowskill tenant black female
## 3998 married midskill husband white male
## 3999 married midskill husband white male
## 4000 married midskill husband white male
## 4001 married highskill husband white male
## 4002 notmarried midskill depchild white male
## 4003 notmarried highskill depchild white female
## 4004 notmarried other tenant white male
## 4005 notmarried highskill tenant white female
## 4006 married midskill husband white male
## 4007 notmarried highskill tenant Asian_Pac_Islander male
## 4008 divorce lowskill tenant white female
## 4009 married lowskill husband white male
## 4010 divorce highskill tenant white female
## 4011 notmarried clerical depchild white female
## 4012 notmarried highskill tenant white female
## 4013 married lowskill husband white male
## 4014 notmarried clerical depchild white female
## 4015 notmarried highskill depchild white male
## 4016 married highskill husband white male
## 4017 divorce midskill depchild white male
## 4018 married other husband white male
## 4019 married other husband white male
## 4020 notmarried highskill tenant white male
## 4021 divorce clerical single_occ white female
## 4022 notmarried other depchild white female
## 4023 divorce highskill depchild white female
## 4024 notmarried clerical relative white female
## 4025 married highskill husband white male
## 4026 married midskill husband white male
## 4027 notmarried highskill tenant black male
## 4028 married agriculture husband white male
## 4029 divorce lowskill tenant white female
## 4030 married highskill husband white male
## 4031 notmarried highskill tenant white male
## 4032 married highskill husband white male
## 4033 married midskill husband white male
## 4034 notmarried lowskill tenant black male
## 4035 notmarried midskill tenant white male
## 4036 divorce highskill single_occ white male
## 4037 notmarried highskill depchild white female
## 4038 divorce lowskill tenant white female
## 4039 divorce midskill depchild white male
## 4040 divorce midskill tenant white male
## 4041 notmarried highskill tenant white female
## 4042 divorce clerical single_occ black female
## 4043 married lowskill wife white female
## 4044 divorce lowskill tenant white female
## 4045 notmarried highskill depchild white female
## 4046 married midskill husband white male
## 4047 notmarried midskill relative black male
## 4048 widowed lowskill single_occ white female
## 4049 married midskill husband white male
## 4050 divorce midskill tenant native male
## 4051 married highskill husband white male
## 4052 notmarried highskill tenant black male
## 4053 divorce midskill tenant white male
## 4054 married midskill husband white male
## 4055 married highskill husband white male
## 4056 married highskill husband Asian_Pac_Islander male
## 4057 married lowskill husband white male
## 4058 notmarried clerical single_occ black female
## 4059 married midskill husband white male
## 4060 notmarried clerical depchild Asian_Pac_Islander female
## 4061 married highskill husband white male
## 4062 divorce highskill depchild white male
## 4063 notmarried highskill depchild white female
## 4064 divorce clerical tenant white female
## 4065 divorce highskill tenant white male
## 4066 married highskill husband white male
## 4067 married lowskill wife Asian_Pac_Islander female
## 4068 divorce lowskill tenant white female
## 4069 notmarried highskill depchild white male
## 4070 notmarried lowskill single_occ white female
## 4071 notmarried lowskill depchild white male
## 4072 divorce clerical single_occ white female
## 4073 notmarried other single_occ black female
## 4074 divorce highskill single_occ white female
## 4075 notmarried clerical tenant white female
## 4076 married highskill husband white male
## 4077 notmarried midskill depchild black female
## 4078 married highskill husband white male
## 4079 notmarried highskill depchild white male
## 4080 married highskill husband white male
## 4081 notmarried other depchild white male
## 4082 notmarried lowskill depchild white female
## 4083 married midskill husband white male
## 4084 divorce lowskill depchild white female
## 4085 divorce midskill single_occ white female
## 4086 divorce lowskill relative white female
## 4087 married other husband white male
## 4088 married highskill husband white male
## 4089 married highskill husband white male
## 4090 notmarried clerical depchild white male
## 4091 notmarried other depchild white female
## 4092 divorce midskill tenant white male
## 4093 notmarried highskill depchild white male
## 4094 married highskill husband white male
## 4095 married midskill husband white male
## 4096 married highskill husband white male
## 4097 married lowskill husband white male
## 4098 married highskill husband white male
## 4099 notmarried lowskill depchild white male
## 4100 married midskill husband white male
## 4101 notmarried midskill single_occ white male
## 4102 married lowskill husband white male
## 4103 married midskill husband black male
## 4104 notmarried lowskill tenant white male
## 4105 married highskill husband white male
## 4106 divorce lowskill tenant white male
## 4107 notmarried highskill tenant white female
## 4108 married lowskill tenant white male
## 4109 notmarried highskill tenant Asian_Pac_Islander male
## 4110 widowed other relative white female
## 4111 notmarried clerical tenant white female
## 4112 divorce midskill tenant white male
## 4113 divorce highskill single_occ native female
## 4114 notmarried lowskill tenant white female
## 4115 notmarried clerical tenant white female
## 4116 married highskill husband white male
## 4117 married midskill husband white male
## 4118 notmarried highskill depchild white male
## 4119 notmarried highskill tenant white male
## 4120 married midskill husband white male
## 4121 divorce clerical tenant white female
## 4122 married clerical husband white male
## 4123 married midskill husband white male
## 4124 divorce lowskill depchild white female
## 4125 notmarried highskill tenant white male
## 4126 married midskill husband white male
## 4127 married highskill husband black male
## 4128 married highskill husband white male
## 4129 notmarried highskill depchild white female
## 4130 notmarried lowskill tenant white female
## 4131 notmarried midskill single_occ white male
## 4132 married highskill husband white male
## 4133 married agriculture husband white male
## 4134 married midskill husband white male
## 4135 divorce clerical single_occ white female
## 4136 notmarried clerical single_occ black female
## 4137 married highskill husband white male
## 4138 married midskill husband white male
## 4139 notmarried clerical depchild white female
## 4140 notmarried lowskill depchild white male
## 4141 married highskill husband white male
## 4142 divorce midskill tenant white male
## 4143 notmarried clerical tenant white female
## 4144 married highskill husband white male
## 4145 divorce midskill single_occ white female
## 4146 married highskill wife white female
## 4147 married midskill husband white male
## 4148 married other husband white male
## 4149 married agriculture husband white male
## 4150 notmarried lowskill depchild white male
## 4151 divorce clerical tenant white male
## 4152 divorce highskill single_occ white female
## 4153 notmarried other depchild white male
## 4154 notmarried highskill tenant white male
## 4155 married highskill husband white male
## 4156 notmarried other relative black male
## 4157 notmarried clerical depchild white female
## 4158 divorce lowskill tenant white male
## 4159 divorce lowskill single_occ white female
## 4160 married highskill husband black male
## 4161 married midskill depchild white male
## 4162 married midskill husband white male
## 4163 married midskill husband white male
## 4164 married highskill single_occ white male
## 4165 married lowskill wife white female
## 4166 married highskill wife white female
## 4167 notmarried lowskill depchild white female
## 4168 married highskill husband white male
## 4169 notmarried lowskill tenant Asian_Pac_Islander female
## 4170 married other wife native female
## 4171 married highskill husband white male
## 4172 divorce lowskill single_occ white female
## 4173 married highskill husband white male
## 4174 notmarried agriculture depchild white female
## 4175 notmarried other depchild white female
## 4176 divorce lowskill tenant white male
## 4177 married midskill husband white male
## 4178 married highskill husband white male
## 4179 married clerical husband white male
## 4180 notmarried highskill tenant white female
## 4181 notmarried midskill depchild black male
## 4182 widowed lowskill tenant white female
## 4183 married highskill husband white male
## 4184 notmarried midskill tenant white male
## 4185 notmarried highskill depchild white male
## 4186 married clerical husband white male
## 4187 married midskill husband white male
## 4188 divorce midskill single_occ white female
## 4189 married midskill husband white male
## 4190 married highskill husband white male
## 4191 notmarried midskill tenant black male
## 4192 notmarried midskill tenant black female
## 4193 married midskill husband white male
## 4194 notmarried highskill depchild white male
## 4195 notmarried midskill depchild white male
## 4196 notmarried midskill tenant white male
## 4197 married highskill husband white male
## 4198 married highskill husband Asian_Pac_Islander male
## 4199 married midskill wife white female
## 4200 notmarried lowskill tenant white female
## 4201 notmarried other depchild white male
## 4202 married midskill husband white male
## 4203 divorce highskill tenant white female
## 4204 divorce highskill tenant white male
## 4205 notmarried agriculture tenant white male
## 4206 notmarried lowskill depchild white female
## 4207 widowed midskill tenant white female
## 4208 divorce highskill tenant white female
## 4209 notmarried highskill tenant white female
## 4210 notmarried highskill depchild white female
## 4211 married highskill husband white male
## 4212 married agriculture husband white male
## 4213 notmarried other tenant white female
## 4214 notmarried midskill depchild white male
## 4215 divorce midskill tenant white male
## 4216 notmarried other tenant white female
## 4217 notmarried highskill relative black male
## 4218 notmarried other depchild white female
## 4219 notmarried highskill tenant white male
## 4220 notmarried highskill tenant white female
## 4221 notmarried clerical tenant white female
## 4222 divorce highskill single_occ white female
## 4223 divorce midskill tenant white male
## 4224 notmarried midskill depchild white male
## 4225 notmarried lowskill depchild white female
## 4226 divorce clerical tenant white male
## 4227 divorce lowskill single_occ white female
## 4228 notmarried lowskill tenant black male
## 4229 divorce clerical single_occ black female
## 4230 married midskill wife white female
## 4231 married midskill husband white male
## 4232 divorce highskill single_occ white male
## 4233 married highskill husband white male
## 4234 notmarried highskill tenant white female
## 4235 widowed highskill single_occ white female
## 4236 notmarried highskill tenant white male
## 4237 married highskill husband white male
## 4238 notmarried midskill depchild black male
## 4239 married highskill husband white male
## 4240 divorce highskill single_occ white female
## 4241 notmarried other tenant white male
## 4242 notmarried highskill tenant white male
## 4243 divorce midskill depchild white male
## 4244 notmarried midskill tenant white male
## 4245 married highskill husband white male
## 4246 notmarried midskill tenant black male
## 4247 married highskill wife white female
## 4248 notmarried lowskill depchild white male
## 4249 notmarried lowskill tenant black female
## 4250 married highskill husband white male
## 4251 notmarried midskill depchild white male
## 4252 divorce highskill tenant white female
## 4253 married midskill tenant white male
## 4254 divorce clerical single_occ black female
## 4255 notmarried lowskill tenant white male
## 4256 married highskill husband white male
## 4257 married clerical relative white female
## 4258 married midskill husband white male
## 4259 married midskill husband white male
## 4260 notmarried lowskill tenant white male
## 4261 divorce highskill tenant white male
## 4262 notmarried lowskill tenant white male
## 4263 notmarried clerical depchild Asian_Pac_Islander female
## 4264 married agriculture wife white female
## 4265 married highskill husband white male
## 4266 married highskill husband white male
## 4267 married highskill husband white male
## 4268 married midskill husband white male
## 4269 notmarried midskill tenant white male
## 4270 notmarried lowskill relative white male
## 4271 divorce clerical tenant white female
## 4272 married highskill husband white male
## 4273 notmarried other depchild white male
## 4274 widowed lowskill single_occ white female
## 4275 divorce highskill tenant white female
## 4276 married lowskill husband white male
## 4277 notmarried highskill tenant white female
## 4278 notmarried highskill depchild white male
## 4279 divorce clerical single_occ white male
## 4280 divorce highskill single_occ white female
## 4281 married lowskill husband white male
## 4282 widowed lowskill single_occ white female
## 4283 married highskill wife black female
## 4284 married lowskill husband white male
## 4285 notmarried midskill depchild white female
## 4286 married midskill husband white male
## 4287 notmarried clerical relative black female
## 4288 married midskill husband native male
## 4289 divorce other tenant white female
## 4290 notmarried highskill tenant white male
## 4291 married midskill husband white male
## 4292 married midskill husband white male
## 4293 notmarried highskill single_occ white female
## 4294 divorce highskill tenant white male
## 4295 notmarried highskill tenant white female
## 4296 married highskill husband black male
## 4297 notmarried lowskill depchild white male
## 4298 married highskill husband white male
## 4299 notmarried lowskill tenant white female
## 4300 notmarried other depchild white female
## 4301 notmarried highskill tenant white female
## 4302 notmarried agriculture single_occ white male
## 4303 married lowskill husband white male
## 4304 married midskill husband Asian_Pac_Islander male
## 4305 married midskill husband white male
## 4306 notmarried clerical depchild white male
## 4307 married highskill husband white male
## 4308 married midskill husband white male
## 4309 married agriculture husband white male
## 4310 widowed other tenant white female
## 4311 notmarried highskill depchild white female
## 4312 married highskill husband white male
## 4313 married highskill husband white male
## 4314 notmarried midskill depchild white male
## 4315 divorce highskill tenant white female
## 4316 married other husband white male
## 4317 widowed midskill tenant white female
## 4318 divorce clerical single_occ black male
## 4319 notmarried clerical tenant white female
## 4320 married highskill husband white male
## 4321 notmarried midskill depchild white male
## 4322 married clerical husband white male
## 4323 divorce highskill tenant white male
## 4324 notmarried midskill tenant white male
## 4325 divorce highskill depchild white female
## 4326 notmarried midskill tenant white male
## 4327 married midskill husband white male
## 4328 married highskill husband white male
## 4329 notmarried lowskill depchild white male
## 4330 divorce highskill tenant white male
## 4331 married highskill husband white male
## 4332 married highskill husband white male
## 4333 married highskill husband white male
## 4334 notmarried highskill tenant white male
## 4335 married highskill husband white male
## 4336 married highskill husband white male
## 4337 notmarried lowskill single_occ black female
## 4338 notmarried other depchild Asian_Pac_Islander male
## 4339 married highskill husband white male
## 4340 married highskill husband white male
## 4341 married clerical depchild white female
## 4342 married midskill husband white male
## 4343 notmarried highskill depchild white female
## 4344 married highskill husband white male
## 4345 married midskill husband white male
## 4346 married highskill husband white male
## 4347 notmarried clerical single_occ white female
## 4348 notmarried highskill single_occ black female
## 4349 divorce lowskill tenant native female
## 4350 married highskill husband white male
## 4351 notmarried lowskill depchild white male
## 4352 married midskill husband white male
## 4353 divorce midskill tenant white male
## 4354 married highskill wife white female
## 4355 married highskill husband white male
## 4356 married midskill husband white male
## 4357 divorce highskill single_occ white male
## 4358 notmarried midskill depchild white male
## 4359 notmarried clerical tenant white female
## 4360 notmarried highskill tenant white female
## 4361 notmarried highskill depchild white male
## 4362 married lowskill husband black male
## 4363 notmarried midskill tenant white female
## 4364 married midskill husband white male
## 4365 notmarried highskill single_occ white female
## 4366 married highskill husband white male
## 4367 married highskill husband white male
## 4368 married midskill husband Asian_Pac_Islander male
## 4369 married highskill wife white female
## 4370 notmarried other depchild white female
## 4371 married midskill husband white male
## 4372 married midskill husband white male
## 4373 notmarried highskill depchild white female
## 4374 notmarried clerical depchild white male
## 4375 married highskill husband white male
## 4376 notmarried midskill tenant white male
## 4377 married highskill husband white male
## 4378 married highskill husband white male
## 4379 married midskill wife white female
## 4380 notmarried highskill depchild white male
## 4381 married highskill husband white male
## 4382 married highskill tenant black male
## 4383 divorce midskill tenant black female
## 4384 notmarried highskill relative white female
## 4385 married midskill husband white male
## 4386 married midskill husband white male
## 4387 divorce midskill tenant black male
## 4388 divorce clerical tenant white female
## 4389 married midskill husband white male
## 4390 married midskill husband white male
## 4391 married highskill wife white female
## 4392 notmarried midskill relative white male
## 4393 married lowskill husband white male
## 4394 notmarried other depchild white male
## 4395 notmarried highskill tenant white male
## 4396 notmarried lowskill relative white male
## 4397 notmarried highskill tenant white female
## 4398 notmarried highskill tenant white female
## 4399 notmarried highskill depchild white male
## 4400 notmarried highskill depchild white female
## 4401 divorce lowskill single_occ white female
## 4402 married highskill wife white female
## 4403 notmarried highskill depchild white female
## 4404 married highskill husband Asian_Pac_Islander male
## 4405 married midskill husband white male
## 4406 married highskill husband white male
## 4407 notmarried clerical depchild white female
## 4408 married highskill husband white male
## 4409 married clerical husband white male
## 4410 notmarried other relative Asian_Pac_Islander female
## 4411 married midskill husband white male
## 4412 married highskill husband white male
## 4413 divorce lowskill depchild white female
## 4414 notmarried other depchild white female
## 4415 notmarried clerical tenant white female
## 4416 divorce midskill tenant white male
## 4417 notmarried clerical depchild white female
## 4418 married clerical husband white male
## 4419 widowed highskill single_occ white female
## 4420 notmarried lowskill relative white male
## 4421 divorce midskill single_occ black male
## 4422 married highskill husband white male
## 4423 widowed other tenant black female
## 4424 divorce midskill single_occ black male
## 4425 divorce highskill single_occ white female
## 4426 married midskill husband white male
## 4427 married midskill husband native male
## 4428 notmarried lowskill tenant white female
## 4429 married clerical husband white male
## 4430 married lowskill husband white male
## 4431 married clerical wife black female
## 4432 notmarried clerical tenant white female
## 4433 divorce highskill tenant white male
## 4434 married highskill husband white male
## 4435 notmarried highskill tenant white male
## 4436 divorce other tenant white female
## 4437 married highskill husband white male
## 4438 notmarried other tenant white female
## 4439 married highskill husband white male
## 4440 notmarried highskill tenant white male
## 4441 married highskill husband white male
## 4442 married highskill husband black male
## 4443 notmarried highskill tenant white female
## 4444 married highskill husband white male
## 4445 notmarried midskill depchild white male
## 4446 married highskill husband white male
## 4447 divorce lowskill tenant black female
## 4448 notmarried highskill depchild white female
## 4449 divorce clerical tenant white male
## 4450 divorce midskill tenant white male
## 4451 notmarried highskill tenant white male
## 4452 notmarried midskill tenant white female
## 4453 notmarried clerical depchild Asian_Pac_Islander female
## 4454 married clerical wife white female
## 4455 notmarried highskill relative white male
## 4456 married midskill husband white male
## 4457 married highskill husband white male
## 4458 divorce highskill depchild other male
## 4459 divorce agriculture single_occ white male
## 4460 notmarried highskill tenant white male
## 4461 divorce other single_occ white female
## 4462 notmarried lowskill depchild white male
## 4463 married highskill husband other male
## 4464 widowed clerical single_occ white female
## 4465 notmarried other tenant white male
## 4466 married highskill husband white male
## 4467 married highskill wife white female
## 4468 married highskill husband white male
## 4469 notmarried midskill depchild white female
## 4470 notmarried highskill depchild white male
## 4471 married midskill husband black male
## 4472 married clerical husband white male
## 4473 married clerical wife white female
## 4474 married midskill husband white male
## 4475 married highskill husband white male
## 4476 married highskill husband white male
## 4477 notmarried midskill tenant white male
## 4478 married lowskill husband white male
## 4479 divorce highskill single_occ white female
## 4480 notmarried highskill tenant white male
## 4481 married highskill husband Asian_Pac_Islander male
## 4482 notmarried highskill tenant white male
## 4483 notmarried highskill single_occ black female
## 4484 notmarried highskill tenant white male
## 4485 widowed clerical single_occ white female
## 4486 married clerical single_occ white female
## 4487 married midskill husband black male
## 4488 notmarried lowskill depchild white male
## 4489 notmarried highskill tenant white female
## 4490 divorce lowskill tenant white male
## 4491 notmarried highskill tenant white male
## 4492 married highskill husband white male
## 4493 married highskill wife white female
## 4494 divorce highskill depchild white female
## 4495 married midskill husband white male
## 4496 married highskill husband white male
## 4497 married agriculture husband white male
## 4498 married midskill husband white male
## 4499 divorce lowskill single_occ black female
## 4500 notmarried other depchild white male
## 4501 divorce other tenant white female
## 4502 notmarried lowskill tenant black male
## 4503 married highskill husband white male
## 4504 notmarried highskill tenant white female
## 4505 divorce highskill single_occ white female
## 4506 notmarried midskill tenant black male
## 4507 notmarried lowskill depchild white male
## 4508 married midskill husband white male
## 4509 notmarried lowskill tenant black male
## 4510 notmarried midskill depchild white female
## 4511 divorce lowskill single_occ white female
## 4512 notmarried highskill tenant white male
## 4513 married midskill relative Asian_Pac_Islander male
## 4514 notmarried lowskill depchild white male
## 4515 divorce highskill relative white female
## 4516 notmarried clerical depchild white male
## 4517 notmarried clerical tenant white female
## 4518 married highskill husband white male
## 4519 married midskill husband white male
## 4520 notmarried other tenant white male
## 4521 married lowskill husband white male
## 4522 married lowskill husband black male
## 4523 notmarried clerical tenant white female
## 4524 married highskill husband Asian_Pac_Islander male
## 4525 married midskill husband white male
## 4526 notmarried highskill tenant white female
## 4527 married midskill wife black female
## 4528 notmarried lowskill tenant white male
## 4529 divorce lowskill single_occ white female
## 4530 married clerical husband white male
## 4531 divorce highskill tenant white female
## 4532 notmarried other depchild white male
## 4533 divorce lowskill depchild Asian_Pac_Islander female
## 4534 notmarried midskill depchild white male
## 4535 notmarried lowskill depchild white female
## 4536 married highskill husband white male
## 4537 notmarried highskill tenant black female
## 4538 married highskill husband white male
## 4539 notmarried highskill depchild Asian_Pac_Islander female
## 4540 married highskill husband white male
## 4541 married clerical wife white female
## 4542 divorce clerical relative white female
## 4543 married agriculture husband white male
## 4544 married agriculture husband white male
## 4545 notmarried midskill tenant white male
## 4546 married highskill wife black female
## 4547 married highskill husband white male
## 4548 married midskill husband white male
## 4549 divorce other tenant white male
## 4550 married highskill husband white male
## 4551 married midskill husband white male
## 4552 notmarried highskill tenant white male
## 4553 married midskill husband black male
## 4554 notmarried other depchild black male
## 4555 notmarried lowskill depchild white male
## 4556 married lowskill husband white male
## 4557 notmarried clerical single_occ white female
## 4558 notmarried highskill tenant white female
## 4559 married midskill husband white male
## 4560 married highskill husband white male
## 4561 notmarried midskill tenant white male
## 4562 married lowskill husband white male
## 4563 notmarried lowskill single_occ white female
## 4564 notmarried highskill tenant white male
## 4565 divorce clerical tenant white female
## 4566 married midskill husband white male
## 4567 married highskill husband white male
## 4568 married highskill husband white male
## 4569 notmarried highskill depchild white male
## 4570 divorce clerical single_occ white female
## 4571 widowed clerical tenant white female
## 4572 married highskill husband white male
## 4573 notmarried lowskill tenant white male
## 4574 notmarried clerical single_occ black female
## 4575 notmarried agriculture tenant white male
## 4576 married highskill wife black female
## 4577 married midskill wife black female
## 4578 married highskill husband white male
## 4579 divorce lowskill tenant white female
## 4580 divorce highskill single_occ white female
## 4581 notmarried lowskill depchild white male
## 4582 divorce midskill single_occ white male
## 4583 notmarried clerical tenant white female
## 4584 divorce highskill single_occ Asian_Pac_Islander female
## 4585 married highskill husband white male
## 4586 notmarried lowskill tenant white male
## 4587 notmarried clerical tenant white female
## 4588 divorce clerical tenant white female
## 4589 married lowskill wife white female
## 4590 notmarried midskill tenant white female
## 4591 married highskill husband white male
## 4592 divorce midskill depchild white male
## 4593 married other wife white female
## 4594 married highskill husband white male
## 4595 notmarried lowskill depchild white male
## 4596 married highskill husband black male
## 4597 notmarried midskill tenant Asian_Pac_Islander male
## 4598 married highskill husband white male
## 4599 married highskill tenant Asian_Pac_Islander female
## 4600 married midskill husband white male
## 4601 married midskill husband white male
## 4602 divorce lowskill single_occ black female
## 4603 divorce midskill single_occ black female
## 4604 married midskill husband white male
## 4605 divorce clerical single_occ white female
## 4606 notmarried highskill depchild white female
## 4607 married other wife white female
## 4608 notmarried other single_occ white female
## 4609 divorce highskill tenant white female
## 4610 married lowskill wife white female
## 4611 married midskill husband Asian_Pac_Islander male
## 4612 married highskill husband native male
## 4613 married highskill husband white male
## 4614 married other husband black male
## 4615 married agriculture husband white male
## 4616 notmarried midskill depchild black male
## 4617 divorce clerical tenant white female
## 4618 notmarried highskill relative Asian_Pac_Islander male
## 4619 married highskill husband white male
## 4620 divorce highskill relative white male
## 4621 notmarried highskill tenant white male
## 4622 notmarried other depchild white male
## 4623 married highskill husband white male
## 4624 notmarried lowskill depchild white female
## 4625 notmarried highskill single_occ white female
## 4626 married highskill husband Asian_Pac_Islander male
## 4627 divorce lowskill depchild white male
## 4628 married other husband white male
## 4629 notmarried midskill tenant white male
## 4630 married clerical wife white female
## 4631 married midskill husband white male
## 4632 notmarried clerical depchild white male
## 4633 married midskill husband white male
## 4634 married lowskill single_occ white female
## 4635 married highskill husband black male
## 4636 notmarried highskill depchild white male
## 4637 married midskill husband white male
## 4638 married midskill husband white male
## 4639 divorce lowskill tenant black female
## 4640 notmarried highskill tenant black male
## 4641 notmarried midskill depchild white male
## 4642 married highskill husband white male
## 4643 notmarried lowskill relative white male
## 4644 widowed lowskill single_occ black female
## 4645 married lowskill husband white male
## 4646 married clerical husband black male
## 4647 married midskill husband white male
## 4648 notmarried lowskill tenant black male
## 4649 notmarried highskill tenant white female
## 4650 divorce highskill single_occ black female
## 4651 notmarried highskill depchild Asian_Pac_Islander female
## 4652 married highskill husband white male
## 4653 divorce highskill single_occ white female
## 4654 married midskill husband white male
## 4655 married lowskill husband white male
## 4656 notmarried other tenant white female
## 4657 married highskill wife black female
## 4658 married highskill husband white male
## 4659 notmarried lowskill depchild white female
## 4660 married midskill husband white male
## 4661 notmarried lowskill tenant white male
## 4662 married highskill husband white male
## 4663 married highskill husband white male
## 4664 married midskill wife white female
## 4665 notmarried lowskill depchild black male
## 4666 divorce lowskill single_occ white female
## 4667 married highskill husband native male
## 4668 notmarried agriculture depchild white male
## 4669 married highskill husband white male
## 4670 married highskill husband white male
## 4671 married midskill husband white male
## 4672 married highskill husband white male
## 4673 notmarried highskill tenant white female
## 4674 married midskill husband white male
## 4675 married highskill husband white male
## 4676 married highskill husband white male
## 4677 notmarried highskill depchild white male
## 4678 married midskill husband white male
## 4679 notmarried highskill tenant black female
## 4680 notmarried highskill relative white female
## 4681 married highskill husband white male
## 4682 married highskill husband white male
## 4683 married highskill husband white male
## 4684 notmarried midskill tenant black male
## 4685 notmarried clerical tenant white female
## 4686 married other wife white female
## 4687 notmarried highskill depchild white male
## 4688 divorce midskill single_occ black female
## 4689 notmarried other depchild white female
## 4690 married midskill husband white male
## 4691 notmarried highskill depchild black male
## 4692 married clerical husband white male
## 4693 divorce highskill tenant white female
## 4694 notmarried highskill depchild white male
## 4695 married midskill husband white male
## 4696 married lowskill husband white male
## 4697 married highskill husband white male
## 4698 notmarried lowskill relative white male
## 4699 married midskill tenant white male
## 4700 married midskill husband white male
## 4701 notmarried midskill relative other male
## 4702 notmarried lowskill tenant white male
## 4703 notmarried highskill depchild white female
## 4704 divorce lowskill tenant white male
## 4705 married midskill wife white female
## 4706 divorce midskill single_occ white female
## 4707 married agriculture husband white male
## 4708 married midskill husband white male
## 4709 notmarried lowskill depchild white male
## 4710 married highskill husband white male
## 4711 divorce midskill tenant white male
## 4712 married highskill husband white male
## 4713 notmarried clerical depchild white female
## 4714 divorce midskill single_occ white male
## 4715 notmarried lowskill depchild white female
## 4716 notmarried clerical tenant white male
## 4717 notmarried lowskill single_occ black female
## 4718 married midskill husband white male
## 4719 widowed lowskill single_occ other female
## 4720 divorce highskill tenant white female
## 4721 widowed lowskill tenant white female
## 4722 married other husband white male
## 4723 notmarried highskill depchild white male
## 4724 married midskill husband white male
## 4725 married highskill husband Asian_Pac_Islander male
## 4726 notmarried midskill relative white male
## 4727 married midskill husband white male
## 4728 married highskill husband white male
## 4729 notmarried highskill depchild white female
## 4730 widowed other tenant white female
## 4731 divorce lowskill single_occ white female
## 4732 divorce midskill relative white male
## 4733 notmarried agriculture single_occ other male
## 4734 notmarried clerical single_occ white male
## 4735 notmarried lowskill relative white female
## 4736 notmarried highskill tenant white male
## 4737 married highskill husband white male
## 4738 notmarried midskill depchild white male
## 4739 notmarried midskill tenant black male
## 4740 notmarried highskill tenant white female
## 4741 notmarried highskill tenant white male
## 4742 notmarried highskill tenant white female
## 4743 married highskill husband white male
## 4744 married highskill husband white male
## 4745 notmarried highskill tenant white male
## 4746 widowed other single_occ white female
## 4747 notmarried highskill tenant white male
## 4748 married highskill husband white male
## 4749 divorce highskill single_occ white male
## 4750 divorce highskill tenant black female
## 4751 married highskill husband white male
## 4752 married highskill husband white male
## 4753 married other husband white male
## 4754 married midskill husband black male
## 4755 divorce highskill single_occ white female
## 4756 married other husband white male
## 4757 married midskill husband white male
## 4758 married midskill husband white male
## 4759 notmarried midskill single_occ white male
## 4760 widowed highskill single_occ black female
## 4761 divorce highskill tenant black female
## 4762 married highskill husband white male
## 4763 notmarried clerical tenant white female
## 4764 divorce highskill tenant white male
## 4765 divorce highskill tenant white male
## 4766 married midskill husband white male
## 4767 married other husband white male
## 4768 notmarried highskill depchild white female
## 4769 married highskill husband Asian_Pac_Islander male
## 4770 notmarried midskill tenant white female
## 4771 notmarried lowskill depchild white female
## 4772 married lowskill husband white male
## 4773 divorce lowskill relative white female
## 4774 notmarried agriculture depchild white male
## 4775 notmarried highskill tenant white female
## 4776 notmarried midskill depchild white male
## 4777 married highskill husband white male
## 4778 notmarried highskill tenant white male
## 4779 divorce other depchild white male
## 4780 notmarried highskill depchild Asian_Pac_Islander male
## 4781 married highskill husband white male
## 4782 divorce midskill depchild white male
## 4783 married other husband white male
## 4784 notmarried lowskill depchild white male
## 4785 married highskill husband white male
## 4786 notmarried midskill relative Asian_Pac_Islander female
## 4787 notmarried midskill depchild Asian_Pac_Islander male
## 4788 married midskill husband white male
## 4789 married highskill husband white male
## 4790 married lowskill husband white male
## 4791 married highskill husband white male
## 4792 divorce clerical single_occ white female
## 4793 married midskill husband white male
## 4794 married lowskill husband black male
## 4795 married midskill husband white male
## 4796 married highskill husband white male
## 4797 notmarried clerical depchild black female
## 4798 married highskill husband white male
## 4799 notmarried midskill tenant Asian_Pac_Islander male
## 4800 notmarried clerical tenant white male
## 4801 married midskill husband white male
## 4802 notmarried other tenant white male
## 4803 married midskill husband white male
## 4804 notmarried lowskill tenant Asian_Pac_Islander female
## 4805 married agriculture husband white male
## 4806 notmarried highskill tenant white female
## 4807 divorce midskill tenant white male
## 4808 divorce highskill single_occ white male
## 4809 married clerical husband white male
## 4810 notmarried highskill depchild white male
## 4811 divorce highskill tenant white male
## 4812 notmarried highskill tenant white female
## 4813 divorce other tenant white female
## 4814 married midskill husband white male
## 4815 notmarried clerical depchild white female
## 4816 married clerical wife white female
## 4817 married highskill husband white male
## 4818 notmarried highskill tenant white female
## 4819 notmarried other depchild white female
## 4820 divorce clerical single_occ white female
## 4821 married midskill husband white male
## 4822 divorce highskill single_occ white female
## 4823 notmarried highskill tenant black male
## 4824 married midskill husband white male
## 4825 divorce lowskill single_occ black male
## 4826 married clerical husband white male
## 4827 notmarried clerical single_occ Asian_Pac_Islander female
## 4828 married midskill husband white male
## 4829 divorce lowskill tenant white female
## 4830 divorce lowskill single_occ white female
## 4831 married midskill relative white male
## 4832 widowed clerical tenant white female
## 4833 married midskill husband white male
## 4834 married highskill husband white male
## 4835 divorce clerical tenant white female
## 4836 notmarried other single_occ black female
## 4837 divorce highskill tenant white male
## 4838 notmarried clerical tenant white female
## 4839 married other husband white male
## 4840 notmarried highskill single_occ black female
## 4841 notmarried lowskill single_occ white male
## 4842 divorce clerical relative black female
## 4843 divorce highskill tenant white female
## 4844 married midskill husband white male
## 4845 notmarried lowskill single_occ white male
## 4846 married clerical husband white male
## 4847 married midskill husband white male
## 4848 married highskill husband white male
## 4849 divorce highskill tenant white female
## 4850 married agriculture husband white male
## 4851 married midskill husband white male
## 4852 married highskill husband white male
## 4853 married highskill husband white male
## 4854 notmarried lowskill depchild white male
## 4855 notmarried highskill tenant Asian_Pac_Islander female
## 4856 notmarried clerical depchild white female
## 4857 notmarried highskill tenant white female
## 4858 married highskill husband white male
## 4859 married midskill husband white male
## 4860 notmarried agriculture relative white male
## 4861 divorce highskill single_occ black female
## 4862 notmarried midskill relative white male
## 4863 married midskill husband white male
## 4864 married highskill husband white male
## 4865 notmarried midskill depchild black female
## 4866 married highskill husband white male
## 4867 notmarried midskill depchild white male
## 4868 married midskill husband white male
## 4869 notmarried highskill relative white male
## 4870 divorce lowskill tenant white female
## 4871 married highskill husband white male
## 4872 notmarried clerical depchild white female
## 4873 married highskill husband white male
## 4874 married highskill husband white male
## 4875 married clerical wife white female
## 4876 married highskill husband white male
## 4877 notmarried highskill depchild white male
## 4878 divorce clerical single_occ black female
## 4879 married highskill wife white female
## 4880 divorce highskill tenant black male
## 4881 notmarried midskill tenant white male
## 4882 notmarried midskill tenant white male
## 4883 married midskill husband white male
## 4884 notmarried highskill tenant white male
## 4885 notmarried other single_occ white female
## 4886 married highskill husband white male
## 4887 divorce highskill single_occ white female
## 4888 notmarried clerical single_occ black female
## 4889 married highskill husband white male
## 4890 married midskill husband white male
## 4891 married agriculture husband white male
## 4892 notmarried lowskill tenant white female
## 4893 notmarried lowskill depchild white female
## 4894 notmarried highskill tenant white female
## 4895 married highskill husband white male
## 4896 notmarried agriculture tenant white male
## 4897 notmarried highskill tenant white male
## 4898 married highskill husband white male
## 4899 married highskill husband white male
## 4900 notmarried highskill tenant white male
## 4901 married midskill husband white male
## 4902 notmarried highskill tenant white male
## 4903 married midskill husband white male
## 4904 married midskill husband Asian_Pac_Islander male
## 4905 notmarried lowskill depchild white male
## 4906 married highskill wife white female
## 4907 widowed highskill tenant white female
## 4908 notmarried clerical tenant white female
## 4909 married midskill husband white male
## 4910 widowed highskill single_occ white female
## 4911 married midskill husband white male
## 4912 notmarried clerical depchild white female
## 4913 notmarried clerical depchild black female
## 4914 notmarried lowskill relative white female
## 4915 married midskill husband white male
## 4916 notmarried highskill depchild white male
## 4917 married lowskill husband white male
## 4918 notmarried highskill tenant white female
## 4919 married highskill husband white male
## 4920 notmarried clerical relative Asian_Pac_Islander female
## 4921 married clerical wife white female
## 4922 married highskill husband white male
## 4923 notmarried clerical tenant white male
## 4924 married highskill husband white male
## 4925 divorce highskill single_occ white female
## 4926 notmarried other tenant white male
## 4927 divorce highskill single_occ white female
## 4928 married clerical depchild white female
## 4929 divorce highskill tenant white male
## 4930 divorce midskill single_occ white male
## 4931 notmarried lowskill depchild white male
## 4932 notmarried lowskill depchild white female
## 4933 divorce midskill depchild white male
## 4934 divorce lowskill single_occ black female
## 4935 married lowskill husband white male
## 4936 married clerical husband black male
## 4937 widowed clerical tenant white female
## 4938 notmarried clerical depchild white male
## 4939 married clerical wife white female
## 4940 married clerical depchild white female
## 4941 married midskill husband white male
## 4942 married midskill husband white male
## 4943 notmarried other depchild white female
## 4944 married highskill husband white male
## 4945 married highskill husband white male
## 4946 married highskill husband white male
## 4947 notmarried highskill tenant white male
## 4948 married midskill husband white male
## 4949 divorce highskill tenant native female
## 4950 married highskill husband white male
## 4951 married highskill husband white male
## 4952 married highskill husband white male
## 4953 married highskill husband white male
## 4954 married clerical husband white male
## 4955 divorce highskill tenant white female
## 4956 married lowskill husband white male
## 4957 widowed highskill single_occ white male
## 4958 divorce lowskill depchild white female
## 4959 notmarried other depchild white female
## 4960 widowed clerical tenant white male
## 4961 notmarried highskill depchild white female
## 4962 married highskill wife white female
## 4963 notmarried midskill depchild other male
## 4964 notmarried highskill tenant white female
## 4965 married midskill husband white male
## 4966 notmarried highskill tenant white male
## 4967 notmarried midskill depchild black male
## 4968 married highskill wife white female
## 4969 notmarried highskill relative white male
## 4970 notmarried highskill depchild white male
## 4971 married other husband white male
## 4972 married midskill husband white male
## 4973 married highskill husband Asian_Pac_Islander male
## 4974 married agriculture husband white male
## 4975 married highskill husband white male
## 4976 married clerical husband white male
## 4977 married midskill husband white male
## 4978 married midskill husband white male
## 4979 divorce highskill tenant white male
## 4980 married lowskill tenant white male
## 4981 divorce other depchild black male
## 4982 notmarried clerical tenant white male
## 4983 notmarried other depchild white male
## 4984 married highskill husband white male
## 4985 notmarried midskill depchild black male
## 4986 notmarried lowskill single_occ black male
## 4987 notmarried lowskill depchild white female
## 4988 married highskill husband white male
## 4989 married highskill husband white male
## 4990 divorce highskill single_occ black female
## 4991 divorce highskill tenant white male
## 4992 married midskill husband white male
## 4993 married lowskill husband white male
## 4994 notmarried highskill tenant white male
## 4995 married highskill husband white male
## 4996 notmarried midskill single_occ white female
## 4997 married midskill husband white male
## 4998 married midskill husband white male
## 4999 notmarried clerical tenant white male
## 5000 notmarried highskill tenant white female
## 5001 divorce highskill tenant white male
## 5002 notmarried highskill relative white female
## 5003 married highskill wife white female
## 5004 notmarried midskill single_occ white female
## 5005 married midskill husband black male
## 5006 married highskill husband white male
## 5007 notmarried lowskill tenant white male
## 5008 married highskill husband white male
## 5009 married midskill husband white male
## 5010 married highskill husband white male
## 5011 notmarried highskill tenant white female
## 5012 notmarried midskill tenant white male
## 5013 notmarried highskill tenant Asian_Pac_Islander male
## 5014 married highskill husband white male
## 5015 divorce midskill tenant white male
## 5016 married midskill husband black male
## 5017 divorce highskill single_occ white female
## 5018 divorce clerical depchild black female
## 5019 notmarried highskill tenant white male
## 5020 notmarried lowskill tenant white male
## 5021 notmarried other depchild white female
## 5022 married midskill husband white male
## 5023 married midskill husband native male
## 5024 notmarried clerical tenant white female
## 5025 married clerical husband white male
## 5026 married highskill husband white male
## 5027 married highskill husband white male
## 5028 married highskill wife black female
## 5029 married agriculture husband other male
## 5030 notmarried highskill depchild white female
## 5031 notmarried lowskill depchild white male
## 5032 married highskill husband white male
## 5033 notmarried lowskill tenant white male
## 5034 married highskill husband white male
## 5035 married agriculture husband white male
## 5036 married lowskill wife white female
## 5037 married clerical wife black female
## 5038 married highskill husband white male
## 5039 married midskill husband white male
## 5040 married midskill husband white male
## 5041 notmarried highskill tenant white female
## 5042 married midskill husband white male
## 5043 divorce clerical tenant white female
## 5044 divorce highskill tenant white female
## 5045 married highskill husband white male
## 5046 married highskill husband white male
## 5047 married midskill husband white male
## 5048 notmarried midskill tenant white male
## 5049 divorce agriculture tenant white male
## 5050 married highskill husband white male
## 5051 married highskill husband white male
## 5052 married highskill husband white male
## 5053 divorce highskill tenant white female
## 5054 married highskill husband white male
## 5055 notmarried midskill depchild white male
## 5056 notmarried lowskill depchild white male
## 5057 married midskill husband white male
## 5058 married lowskill husband white male
## 5059 married highskill husband black male
## 5060 divorce clerical tenant white female
## 5061 married midskill husband white male
## 5062 notmarried midskill depchild white male
## 5063 married midskill husband white male
## 5064 married midskill husband white male
## 5065 notmarried other depchild white female
## 5066 married midskill tenant white male
## 5067 married highskill husband white male
## 5068 married lowskill depchild white male
## 5069 notmarried midskill tenant white male
## 5070 notmarried highskill tenant white female
## 5071 notmarried highskill depchild white male
## 5072 married agriculture husband white male
## 5073 married highskill husband white male
## 5074 married highskill husband white male
## 5075 married highskill husband white male
## 5076 married highskill wife white female
## 5077 notmarried lowskill depchild black female
## 5078 divorce lowskill tenant white female
## 5079 married midskill husband white male
## 5080 married midskill husband white male
## 5081 divorce highskill single_occ black female
## 5082 notmarried midskill relative Asian_Pac_Islander male
## 5083 divorce highskill tenant white male
## 5084 notmarried clerical tenant black female
## 5085 notmarried highskill tenant black female
## 5086 married agriculture husband white male
## 5087 married midskill husband white male
## 5088 notmarried midskill depchild white male
## 5089 married midskill husband white male
## 5090 married highskill husband white male
## 5091 notmarried lowskill depchild white male
## 5092 divorce highskill single_occ white female
## 5093 divorce midskill depchild white male
## 5094 married midskill husband white male
## 5095 divorce midskill tenant white female
## 5096 married clerical wife white female
## 5097 widowed lowskill single_occ black female
## 5098 married highskill husband white male
## 5099 married highskill husband white male
## 5100 married highskill husband white male
## 5101 married agriculture husband white male
## 5102 married highskill husband white male
## 5103 married lowskill husband white male
## 5104 married highskill husband white male
## 5105 notmarried lowskill tenant Asian_Pac_Islander male
## 5106 married midskill tenant white male
## 5107 married highskill husband white male
## 5108 notmarried midskill depchild white male
## 5109 married highskill husband white male
## 5110 married lowskill tenant white male
## 5111 notmarried midskill depchild white male
## 5112 notmarried lowskill single_occ black male
## 5113 married midskill husband white male
## 5114 widowed midskill single_occ white female
## 5115 married highskill husband white male
## 5116 notmarried highskill relative black male
## 5117 married midskill husband white male
## 5118 notmarried highskill tenant white male
## 5119 married highskill husband other male
## 5120 divorce lowskill single_occ black female
## 5121 married highskill husband white male
## 5122 married midskill husband white male
## 5123 notmarried lowskill tenant white male
## 5124 married highskill wife white female
## 5125 notmarried highskill depchild white female
## 5126 notmarried highskill tenant white male
## 5127 notmarried midskill relative white female
## 5128 widowed highskill tenant native female
## 5129 notmarried highskill depchild black female
## 5130 married highskill husband white male
## 5131 divorce midskill tenant white male
## 5132 married clerical wife white female
## 5133 notmarried highskill tenant white female
## 5134 widowed midskill tenant white female
## 5135 married lowskill husband white male
## 5136 widowed midskill single_occ white male
## 5137 notmarried lowskill depchild white male
## 5138 married clerical wife white female
## 5139 notmarried highskill depchild white male
## 5140 married lowskill husband black male
## 5141 married highskill husband white male
## 5142 notmarried midskill depchild white male
## 5143 notmarried lowskill tenant black female
## 5144 divorce clerical relative white female
## 5145 married highskill husband white male
## 5146 divorce highskill single_occ white female
## 5147 notmarried highskill tenant white male
## 5148 notmarried other depchild black male
## 5149 married midskill husband white male
## 5150 married midskill husband black male
## 5151 married midskill husband white male
## 5152 divorce highskill single_occ white female
## 5153 notmarried clerical tenant white female
## 5154 divorce clerical single_occ white female
## 5155 married midskill husband white male
## 5156 divorce agriculture single_occ white female
## 5157 widowed other tenant white female
## 5158 married clerical wife white female
## 5159 married highskill husband white male
## 5160 married midskill husband white male
## 5161 married highskill wife white female
## 5162 married clerical wife black female
## 5163 married lowskill husband white male
## 5164 notmarried highskill depchild white male
## 5165 married midskill husband white male
## 5166 notmarried highskill tenant white male
## 5167 married clerical husband white male
## 5168 married lowskill husband white male
## 5169 married agriculture husband white male
## 5170 married agriculture husband white male
## 5171 married midskill husband white male
## 5172 notmarried highskill tenant white male
## 5173 married other wife white female
## 5174 married highskill husband white male
## 5175 notmarried highskill depchild white male
## 5176 married highskill husband white male
## 5177 married highskill depchild white male
## 5178 divorce clerical tenant white female
## 5179 divorce midskill tenant black female
## 5180 married highskill wife white female
## 5181 divorce highskill single_occ white female
## 5182 notmarried midskill depchild white male
## 5183 married lowskill depchild white male
## 5184 divorce highskill tenant white male
## 5185 married highskill wife white female
## 5186 notmarried highskill depchild white male
## 5187 married highskill husband white male
## 5188 widowed highskill tenant white female
## 5189 notmarried midskill tenant black male
## 5190 married clerical wife black female
## 5191 married midskill husband white male
## 5192 notmarried lowskill depchild white female
## 5193 married highskill husband white male
## 5194 widowed other tenant white female
## 5195 married midskill husband white male
## 5196 divorce clerical single_occ white female
## 5197 married highskill husband Asian_Pac_Islander male
## 5198 married highskill husband white male
## 5199 married other husband white male
## 5200 divorce clerical single_occ white female
## 5201 notmarried highskill tenant white male
## 5202 married highskill husband white male
## 5203 married highskill husband white male
## 5204 notmarried highskill depchild white male
## 5205 notmarried midskill tenant white male
## 5206 notmarried highskill tenant white male
## 5207 notmarried lowskill tenant white male
## 5208 divorce other relative other female
## 5209 notmarried lowskill single_occ black female
## 5210 notmarried other depchild white female
## 5211 notmarried highskill relative black female
## 5212 married midskill husband white male
## 5213 notmarried agriculture tenant white male
## 5214 notmarried highskill relative white female
## 5215 widowed other tenant native female
## 5216 married highskill husband white male
## 5217 notmarried highskill tenant white male
## 5218 notmarried midskill tenant white female
## 5219 notmarried highskill tenant white male
## 5220 married highskill wife Asian_Pac_Islander female
## 5221 married highskill husband white male
## 5222 notmarried clerical depchild white male
## 5223 married highskill husband white male
## 5224 divorce clerical tenant white female
## 5225 married highskill husband white male
## 5226 married midskill husband white male
## 5227 married midskill husband white male
## 5228 married highskill wife black female
## 5229 notmarried other tenant white female
## 5230 married lowskill husband white male
## 5231 married lowskill husband white male
## 5232 divorce highskill tenant white male
## 5233 divorce lowskill tenant white male
## 5234 married highskill husband white male
## 5235 married clerical wife white female
## 5236 notmarried highskill tenant white female
## 5237 married highskill husband white male
## 5238 notmarried highskill depchild white female
## 5239 married highskill husband white male
## 5240 notmarried highskill depchild white female
## 5241 notmarried highskill depchild white female
## 5242 married lowskill tenant white female
## 5243 married highskill husband white male
## 5244 divorce highskill single_occ white female
## 5245 married midskill husband white male
## 5246 divorce highskill tenant white male
## 5247 divorce clerical tenant white female
## 5248 notmarried lowskill depchild white male
## 5249 married midskill husband white male
## 5250 married highskill husband white male
## 5251 married highskill husband white male
## 5252 married highskill wife white female
## 5253 notmarried midskill depchild white female
## 5254 notmarried highskill tenant black male
## 5255 married midskill husband other male
## 5256 divorce other single_occ white female
## 5257 notmarried lowskill depchild white female
## 5258 notmarried midskill tenant white male
## 5259 notmarried clerical depchild white male
## 5260 notmarried lowskill depchild white male
## 5261 notmarried clerical depchild white female
## 5262 notmarried midskill tenant white male
## 5263 divorce highskill tenant white female
## 5264 notmarried lowskill depchild white female
## 5265 married highskill husband white male
## 5266 notmarried clerical tenant white female
## 5267 notmarried midskill depchild white male
## 5268 married highskill husband white male
## 5269 notmarried highskill depchild white female
## 5270 married midskill husband white male
## 5271 divorce midskill tenant white male
## 5272 notmarried midskill relative white male
## 5273 notmarried clerical tenant white female
## 5274 married highskill husband white male
## 5275 married highskill husband white male
## 5276 married highskill husband white male
## 5277 married highskill husband white male
## 5278 notmarried highskill depchild white male
## 5279 married lowskill husband white male
## 5280 divorce highskill tenant white female
## 5281 married highskill husband white male
## 5282 notmarried lowskill tenant white male
## 5283 married highskill husband white male
## 5284 divorce highskill tenant white male
## 5285 notmarried highskill depchild white male
## 5286 married clerical wife white female
## 5287 divorce lowskill relative white male
## 5288 married highskill wife white female
## 5289 notmarried midskill tenant white male
## 5290 married midskill husband white male
## 5291 married highskill husband other male
## 5292 widowed highskill tenant white female
## 5293 notmarried highskill depchild white female
## 5294 married agriculture husband white male
## 5295 divorce other tenant white male
## 5296 married midskill husband white male
## 5297 married other husband white male
## 5298 married lowskill husband white male
## 5299 married clerical wife white female
## 5300 married agriculture husband white male
## 5301 divorce lowskill single_occ white male
## 5302 married midskill husband white male
## 5303 notmarried other depchild white female
## 5304 notmarried lowskill depchild white female
## 5305 notmarried highskill depchild white female
## 5306 divorce clerical tenant white male
## 5307 married agriculture husband white male
## 5308 divorce other single_occ white female
## 5309 notmarried lowskill depchild white male
## 5310 notmarried midskill single_occ white male
## 5311 divorce highskill tenant white female
## 5312 notmarried highskill tenant white male
## 5313 married highskill husband white male
## 5314 notmarried clerical tenant black female
## 5315 married agriculture husband white male
## 5316 notmarried midskill depchild white male
## 5317 notmarried clerical depchild white male
## 5318 married midskill husband white male
## 5319 married highskill husband white male
## 5320 married midskill husband white male
## 5321 notmarried other tenant black male
## 5322 notmarried other depchild white female
## 5323 notmarried highskill tenant white female
## 5324 married midskill husband white male
## 5325 divorce midskill tenant white female
## 5326 notmarried lowskill depchild white male
## 5327 married agriculture husband white male
## 5328 married highskill husband white male
## 5329 notmarried highskill depchild white male
## 5330 married highskill husband white male
## 5331 married lowskill husband Asian_Pac_Islander male
## 5332 notmarried highskill depchild white male
## 5333 notmarried lowskill depchild white male
## 5334 divorce lowskill relative white male
## 5335 married highskill husband white male
## 5336 divorce highskill tenant white female
## 5337 notmarried midskill tenant white male
## 5338 divorce highskill single_occ white female
## 5339 divorce clerical tenant white female
## 5340 notmarried highskill tenant white female
## 5341 married other husband white male
## 5342 notmarried midskill depchild white male
## 5343 married midskill husband white male
## 5344 divorce clerical single_occ black female
## 5345 married other tenant white male
## 5346 married lowskill husband white male
## 5347 notmarried other depchild white male
## 5348 married midskill husband white male
## 5349 notmarried lowskill depchild white male
## 5350 married highskill wife white female
## 5351 notmarried lowskill depchild white male
## 5352 notmarried highskill tenant white male
## 5353 notmarried midskill depchild white male
## 5354 married midskill husband white male
## 5355 divorce highskill single_occ black female
## 5356 married lowskill husband white male
## 5357 notmarried highskill depchild white female
## 5358 married lowskill husband black male
## 5359 married highskill husband native male
## 5360 notmarried lowskill tenant white female
## 5361 notmarried midskill depchild white male
## 5362 notmarried other depchild white male
## 5363 married midskill husband white male
## 5364 notmarried midskill depchild white male
## 5365 married highskill husband white male
## 5366 married lowskill husband white male
## 5367 notmarried highskill depchild white male
## 5368 notmarried highskill depchild white male
## 5369 married highskill husband white male
## 5370 married midskill husband black male
## 5371 married highskill husband white male
## 5372 notmarried highskill relative white male
## 5373 married highskill husband white male
## 5374 married midskill husband white male
## 5375 married highskill husband black male
## 5376 notmarried highskill tenant black female
## 5377 married midskill husband white male
## 5378 divorce highskill tenant white female
## 5379 married midskill husband white male
## 5380 married highskill husband white male
## 5381 married highskill wife black female
## 5382 notmarried clerical tenant white female
## 5383 married highskill husband white male
## 5384 notmarried other depchild white female
## 5385 notmarried other tenant black female
## 5386 divorce lowskill tenant white male
## 5387 married highskill husband white male
## 5388 married clerical wife white female
## 5389 divorce highskill single_occ white male
## 5390 married highskill husband white male
## 5391 divorce lowskill single_occ white female
## 5392 married midskill wife white female
## 5393 widowed clerical tenant white female
## 5394 notmarried highskill tenant white female
## 5395 married highskill husband white male
## 5396 notmarried clerical tenant Asian_Pac_Islander female
## 5397 notmarried lowskill depchild white male
## 5398 notmarried clerical depchild white male
## 5399 divorce midskill tenant white male
## 5400 divorce lowskill single_occ other female
## 5401 married highskill husband white male
## 5402 notmarried midskill relative white male
## 5403 married highskill tenant white female
## 5404 married highskill husband white male
## 5405 married midskill husband white male
## 5406 married highskill husband white male
## 5407 notmarried highskill tenant black male
## 5408 divorce highskill single_occ white female
## 5409 married highskill husband black male
## 5410 divorce highskill tenant white female
## 5411 married clerical relative white female
## 5412 widowed highskill tenant white female
## 5413 notmarried lowskill depchild white male
## 5414 divorce clerical tenant white female
## 5415 married highskill husband white male
## 5416 notmarried clerical depchild Asian_Pac_Islander female
## 5417 married lowskill husband white male
## 5418 married highskill husband white male
## 5419 married midskill husband white male
## 5420 married midskill husband white male
## 5421 married agriculture husband native male
## 5422 married agriculture husband white male
## 5423 notmarried highskill tenant white female
## 5424 divorce lowskill single_occ black female
## 5425 notmarried lowskill tenant Asian_Pac_Islander male
## 5426 married highskill husband white male
## 5427 notmarried lowskill relative white male
## 5428 married clerical husband white male
## 5429 married midskill husband white male
## 5430 divorce highskill tenant white female
## 5431 married midskill husband white male
## 5432 married lowskill wife white female
## 5433 divorce highskill tenant white female
## 5434 divorce clerical tenant white female
## 5435 notmarried clerical depchild black female
## 5436 married midskill tenant Asian_Pac_Islander female
## 5437 notmarried highskill tenant white male
## 5438 married highskill husband white male
## 5439 married midskill husband white male
## 5440 divorce clerical depchild white male
## 5441 notmarried other tenant white female
## 5442 married midskill wife white female
## 5443 married midskill wife white female
## 5444 married highskill husband black male
## 5445 married highskill husband white male
## 5446 divorce midskill depchild white male
## 5447 notmarried other depchild white male
## 5448 notmarried lowskill depchild white female
## 5449 notmarried midskill depchild Asian_Pac_Islander male
## 5450 notmarried lowskill depchild white female
## 5451 notmarried highskill depchild white female
## 5452 divorce midskill tenant white female
## 5453 notmarried clerical depchild white female
## 5454 divorce midskill single_occ white female
## 5455 married clerical wife white female
## 5456 married midskill husband white male
## 5457 married highskill husband white male
## 5458 married clerical husband white male
## 5459 married highskill husband white male
## 5460 notmarried highskill depchild white male
## 5461 notmarried clerical depchild white female
## 5462 notmarried highskill tenant white male
## 5463 married highskill husband white male
## 5464 married midskill tenant white female
## 5465 divorce highskill tenant native female
## 5466 divorce highskill tenant white male
## 5467 notmarried lowskill depchild native male
## 5468 notmarried clerical relative white male
## 5469 divorce lowskill single_occ white male
## 5470 notmarried highskill depchild white male
## 5471 divorce midskill tenant white male
## 5472 widowed other tenant white male
## 5473 notmarried lowskill depchild white male
## 5474 married highskill husband white male
## 5475 married lowskill wife white female
## 5476 married midskill husband white male
## 5477 notmarried lowskill depchild white female
## 5478 notmarried highskill depchild white male
## 5479 married midskill husband white male
## 5480 married highskill husband white male
## 5481 married highskill husband white male
## 5482 married clerical single_occ native female
## 5483 notmarried highskill depchild white male
## 5484 notmarried lowskill tenant white female
## 5485 married highskill husband white male
## 5486 divorce midskill tenant white male
## 5487 married highskill husband white male
## 5488 married highskill husband white male
## 5489 married midskill husband white male
## 5490 divorce highskill tenant white female
## 5491 married highskill husband white male
## 5492 notmarried highskill depchild white male
## 5493 divorce other tenant white female
## 5494 notmarried highskill tenant white male
## 5495 married highskill husband white male
## 5496 notmarried clerical tenant white female
## 5497 divorce midskill tenant white male
## 5498 married clerical husband white male
## 5499 divorce lowskill tenant white female
## 5500 divorce midskill single_occ white male
## 5501 married highskill husband white male
## 5502 divorce highskill relative black male
## 5503 notmarried clerical tenant white female
## 5504 notmarried midskill single_occ white male
## 5505 notmarried clerical single_occ black female
## 5506 married midskill husband black male
## 5507 notmarried midskill depchild white female
## 5508 married midskill husband white male
## 5509 notmarried highskill depchild white female
## 5510 married lowskill husband white male
## 5511 notmarried lowskill depchild other male
## 5512 widowed midskill tenant white female
## 5513 notmarried highskill depchild white female
## 5514 divorce highskill tenant white female
## 5515 notmarried highskill tenant white female
## 5516 notmarried clerical depchild white female
## 5517 married midskill husband white male
## 5518 married midskill husband white male
## 5519 married highskill husband white male
## 5520 notmarried highskill tenant Asian_Pac_Islander female
## 5521 notmarried lowskill tenant black female
## 5522 married midskill husband white male
## 5523 notmarried midskill single_occ white male
## 5524 married clerical wife white female
## 5525 notmarried midskill relative white male
## 5526 notmarried highskill tenant Asian_Pac_Islander female
## 5527 notmarried other depchild white male
## 5528 divorce lowskill single_occ white female
## 5529 divorce highskill depchild white female
## 5530 married other husband white male
## 5531 married highskill wife black female
## 5532 married midskill husband white male
## 5533 divorce highskill tenant white female
## 5534 divorce highskill single_occ white female
## 5535 divorce lowskill single_occ white female
## 5536 notmarried lowskill depchild white male
## 5537 married midskill husband black male
## 5538 married highskill husband white male
## 5539 married highskill husband white male
## 5540 notmarried highskill tenant white male
## 5541 married lowskill husband white male
## 5542 notmarried midskill tenant white male
## 5543 married highskill wife white female
## 5544 married highskill husband white male
## 5545 divorce highskill tenant white male
## 5546 notmarried clerical depchild white male
## 5547 notmarried lowskill tenant black female
## 5548 married highskill husband white male
## 5549 divorce other tenant white male
## 5550 married highskill husband white male
## 5551 married midskill husband white male
## 5552 divorce highskill tenant black male
## 5553 married highskill husband white male
## 5554 notmarried highskill depchild white male
## 5555 married midskill husband white male
## 5556 married highskill husband white male
## 5557 notmarried highskill depchild white female
## 5558 divorce lowskill single_occ white female
## 5559 married highskill husband white male
## 5560 married clerical tenant white female
## 5561 married highskill husband Asian_Pac_Islander male
## 5562 widowed other tenant white female
## 5563 married highskill husband white male
## 5564 divorce midskill tenant white male
## 5565 married highskill husband white male
## 5566 notmarried other relative white female
## 5567 married highskill husband white male
## 5568 divorce midskill tenant white male
## 5569 married midskill husband black male
## 5570 notmarried highskill tenant white female
## 5571 married midskill husband white male
## 5572 divorce clerical relative white female
## 5573 divorce highskill tenant white male
## 5574 married midskill husband white male
## 5575 notmarried highskill tenant white male
## 5576 married agriculture husband white male
## 5577 notmarried midskill tenant white male
## 5578 married midskill husband white male
## 5579 notmarried lowskill tenant white female
## 5580 notmarried midskill tenant white female
## 5581 married highskill husband white male
## 5582 married midskill husband white male
## 5583 married highskill husband white male
## 5584 notmarried midskill depchild white male
## 5585 married highskill husband white male
## 5586 divorce clerical single_occ black female
## 5587 married highskill husband other male
## 5588 married lowskill husband white male
## 5589 married highskill husband white male
## 5590 married highskill husband white male
## 5591 widowed other tenant white female
## 5592 notmarried highskill tenant white female
## 5593 notmarried highskill relative Asian_Pac_Islander male
## 5594 married highskill husband white male
## 5595 notmarried lowskill relative black female
## 5596 married midskill husband white male
## 5597 married highskill husband white male
## 5598 notmarried midskill relative white male
## 5599 notmarried lowskill depchild white male
## 5600 notmarried highskill tenant white male
## 5601 married highskill husband white male
## 5602 married highskill husband white male
## 5603 married highskill husband white male
## 5604 notmarried highskill depchild Asian_Pac_Islander male
## 5605 notmarried lowskill relative white male
## 5606 divorce highskill single_occ black female
## 5607 married highskill husband white male
## 5608 notmarried midskill tenant black male
## 5609 divorce midskill single_occ white male
## 5610 divorce highskill single_occ white female
## 5611 married highskill husband white male
## 5612 married highskill depchild white female
## 5613 married clerical husband white male
## 5614 notmarried highskill depchild black male
## 5615 notmarried midskill tenant white male
## 5616 divorce agriculture tenant white female
## 5617 notmarried lowskill depchild white female
## 5618 notmarried highskill tenant Asian_Pac_Islander female
## 5619 notmarried highskill tenant white male
## 5620 married lowskill husband white male
## 5621 married midskill husband white male
## 5622 notmarried midskill depchild white male
## 5623 divorce lowskill single_occ white female
## 5624 married other husband other male
## 5625 married clerical wife native female
## 5626 married highskill husband white male
## 5627 married lowskill tenant black female
## 5628 divorce clerical single_occ white female
## 5629 married agriculture husband white male
## 5630 married midskill relative Asian_Pac_Islander male
## 5631 divorce highskill tenant white female
## 5632 married lowskill husband white male
## 5633 married other wife white female
## 5634 notmarried clerical depchild white male
## 5635 married highskill husband white male
## 5636 divorce highskill single_occ white male
## 5637 married midskill tenant white male
## 5638 notmarried highskill depchild white female
## 5639 notmarried midskill depchild white male
## 5640 married highskill husband white male
## 5641 married lowskill husband black male
## 5642 married midskill husband white male
## 5643 married highskill husband white male
## 5644 married highskill husband white male
## 5645 divorce highskill tenant white female
## 5646 divorce lowskill tenant white female
## 5647 divorce midskill tenant white male
## 5648 notmarried clerical tenant white male
## 5649 divorce highskill tenant white male
## 5650 married highskill husband white male
## 5651 notmarried midskill depchild white male
## 5652 notmarried midskill tenant white female
## 5653 married other husband white male
## 5654 married lowskill husband white male
## 5655 notmarried highskill tenant white male
## 5656 notmarried highskill tenant black male
## 5657 notmarried lowskill depchild white female
## 5658 married midskill husband white male
## 5659 married highskill wife white female
## 5660 notmarried highskill tenant white male
## 5661 married midskill husband white male
## 5662 notmarried midskill depchild white male
## 5663 married highskill wife white female
## 5664 divorce midskill tenant other male
## 5665 divorce lowskill single_occ white male
## 5666 divorce lowskill tenant black male
## 5667 married midskill husband white male
## 5668 notmarried midskill tenant white female
## 5669 notmarried highskill tenant white male
## 5670 married midskill husband white male
## 5671 notmarried highskill tenant white male
## 5672 married highskill husband black male
## 5673 notmarried highskill tenant white male
## 5674 married clerical husband white male
## 5675 divorce clerical depchild white female
## 5676 divorce highskill tenant white female
## 5677 notmarried lowskill relative white female
## 5678 notmarried highskill tenant white male
## 5679 married clerical single_occ white female
## 5680 married other husband white male
## 5681 married highskill husband white male
## 5682 married highskill husband white male
## 5683 married highskill husband white male
## 5684 divorce highskill tenant white female
## 5685 notmarried midskill depchild white male
## 5686 married highskill husband white male
## 5687 widowed clerical tenant white female
## 5688 married other husband white male
## 5689 married clerical wife white female
## 5690 notmarried lowskill depchild white female
## 5691 married midskill husband white male
## 5692 divorce highskill tenant white male
## 5693 notmarried lowskill depchild white female
## 5694 divorce clerical single_occ white female
## 5695 married highskill husband white male
## 5696 divorce highskill tenant white female
## 5697 married highskill husband white male
## 5698 notmarried clerical single_occ white male
## 5699 married midskill husband Asian_Pac_Islander male
## 5700 notmarried highskill tenant white male
## 5701 married midskill husband white male
## 5702 married midskill husband white male
## 5703 married midskill husband white male
## 5704 married highskill husband white male
## 5705 divorce lowskill single_occ white female
## 5706 married highskill husband white male
## 5707 notmarried midskill tenant white male
## 5708 married highskill husband white male
## 5709 married highskill husband white male
## 5710 married highskill husband white male
## 5711 married clerical husband white male
## 5712 married clerical husband white male
## 5713 married midskill husband white male
## 5714 notmarried highskill tenant white male
## 5715 married midskill husband white male
## 5716 notmarried lowskill depchild white female
## 5717 notmarried lowskill tenant white male
## 5718 married highskill husband white male
## 5719 married agriculture husband white male
## 5720 divorce midskill tenant white female
## 5721 notmarried other depchild black female
## 5722 divorce midskill single_occ white male
## 5723 notmarried highskill depchild white female
## 5724 notmarried clerical relative white female
## 5725 notmarried lowskill depchild white male
## 5726 married highskill wife white female
## 5727 divorce midskill depchild white male
## 5728 divorce midskill tenant white male
## 5729 married highskill husband white male
## 5730 divorce highskill single_occ white female
## 5731 notmarried highskill tenant white male
## 5732 married highskill depchild white male
## 5733 notmarried highskill depchild white female
## 5734 married highskill husband white male
## 5735 married midskill husband white male
## 5736 notmarried lowskill depchild white male
## 5737 married midskill husband white male
## 5738 married midskill husband black male
## 5739 married midskill husband white male
## 5740 notmarried lowskill depchild white male
## 5741 married lowskill husband white male
## 5742 notmarried midskill depchild white male
## 5743 divorce clerical single_occ white female
## 5744 married midskill husband white male
## 5745 notmarried highskill tenant white male
## 5746 married midskill husband white male
## 5747 notmarried highskill depchild white female
## 5748 married highskill husband white male
## 5749 married highskill husband white male
## 5750 married midskill husband white male
## 5751 divorce highskill single_occ black female
## 5752 married midskill husband white male
## 5753 married midskill husband white male
## 5754 married other wife white female
## 5755 married highskill husband white male
## 5756 married lowskill husband white male
## 5757 married midskill husband white male
## 5758 married highskill husband white male
## 5759 notmarried clerical depchild other female
## 5760 divorce highskill relative white female
## 5761 notmarried highskill tenant white female
## 5762 married highskill husband white male
## 5763 married highskill husband white male
## 5764 notmarried lowskill depchild white female
## 5765 married highskill husband white male
## 5766 married midskill wife white female
## 5767 married other wife white female
## 5768 married midskill husband white male
## 5769 notmarried clerical single_occ black female
## 5770 married midskill husband white male
## 5771 notmarried highskill relative black female
## 5772 married highskill husband Asian_Pac_Islander male
## 5773 divorce lowskill tenant white female
## 5774 married midskill husband white male
## 5775 widowed clerical tenant black female
## 5776 divorce highskill tenant native female
## 5777 married midskill husband white male
## 5778 married clerical husband white male
## 5779 notmarried clerical relative white female
## 5780 notmarried highskill tenant black male
## 5781 divorce highskill tenant white female
## 5782 notmarried clerical depchild white female
## 5783 notmarried highskill tenant white female
## 5784 notmarried highskill depchild white female
## 5785 married lowskill wife white female
## 5786 notmarried highskill depchild white female
## 5787 married midskill husband Asian_Pac_Islander male
## 5788 divorce clerical tenant white female
## 5789 divorce other depchild black female
## 5790 divorce lowskill single_occ white female
## 5791 married midskill husband white male
## 5792 married midskill husband white male
## 5793 married highskill husband white male
## 5794 married highskill husband white male
## 5795 married highskill husband white male
## 5796 married midskill husband white male
## 5797 married clerical husband white male
## 5798 notmarried clerical depchild white male
## 5799 married highskill husband white male
## 5800 notmarried midskill depchild white male
## 5801 divorce lowskill tenant white female
## 5802 notmarried midskill tenant white male
## 5803 divorce highskill single_occ white female
## 5804 divorce other depchild white male
## 5805 notmarried agriculture depchild white male
## 5806 notmarried highskill tenant white female
## 5807 married midskill husband white male
## 5808 notmarried lowskill depchild black male
## 5809 married other husband white male
## 5810 divorce highskill single_occ white female
## 5811 married midskill husband white male
## 5812 married highskill wife black female
## 5813 divorce midskill tenant black male
## 5814 married highskill husband white male
## 5815 divorce midskill tenant white male
## 5816 married highskill husband white male
## 5817 married highskill husband white male
## 5818 married highskill husband white male
## 5819 notmarried lowskill tenant white male
## 5820 widowed lowskill single_occ white female
## 5821 married highskill husband white male
## 5822 married midskill husband white male
## 5823 married highskill wife white female
## 5824 married midskill husband white male
## 5825 married highskill husband white male
## 5826 divorce agriculture single_occ white male
## 5827 notmarried lowskill tenant white male
## 5828 married midskill husband white male
## 5829 notmarried highskill depchild white female
## 5830 notmarried lowskill depchild white male
## 5831 widowed midskill tenant white female
## 5832 married highskill husband white male
## 5833 widowed other tenant white female
## 5834 notmarried highskill depchild black female
## 5835 married lowskill husband white male
## 5836 divorce highskill single_occ white male
## 5837 married highskill husband white male
## 5838 divorce highskill tenant white male
## 5839 notmarried clerical tenant white female
## 5840 married highskill husband white male
## 5841 notmarried midskill tenant white female
## 5842 married highskill relative white male
## 5843 notmarried lowskill tenant white female
## 5844 notmarried lowskill depchild white male
## 5845 notmarried lowskill depchild white male
## 5846 married midskill husband white male
## 5847 notmarried lowskill relative native female
## 5848 divorce midskill depchild white male
## 5849 married highskill husband white male
## 5850 notmarried lowskill depchild white male
## 5851 notmarried lowskill depchild white male
## 5852 notmarried clerical depchild black male
## 5853 notmarried midskill depchild white male
## 5854 divorce other single_occ white female
## 5855 married midskill husband white male
## 5856 married agriculture husband white male
## 5857 married midskill husband white male
## 5858 married midskill husband white male
## 5859 notmarried midskill tenant white male
## 5860 notmarried clerical depchild white female
## 5861 married lowskill husband white male
## 5862 married highskill husband white male
## 5863 married highskill husband white male
## 5864 notmarried highskill tenant white female
## 5865 divorce midskill tenant white male
## 5866 notmarried highskill tenant white female
## 5867 married agriculture husband white male
## 5868 married midskill husband white male
## 5869 divorce highskill depchild white female
## 5870 married midskill husband white male
## 5871 married midskill tenant black male
## 5872 married highskill husband white male
## 5873 notmarried lowskill single_occ black female
## 5874 married midskill husband white male
## 5875 divorce midskill tenant white male
## 5876 married midskill husband white male
## 5877 notmarried highskill tenant white male
## 5878 married agriculture husband white male
## 5879 notmarried lowskill tenant native male
## 5880 notmarried midskill depchild white male
## 5881 married midskill husband white male
## 5882 married midskill husband black male
## capital_gain capital_loss hours_per_week native_region income
## 1 2174 0 40 NorthAmerica <=50K
## 2 0 0 13 NorthAmerica <=50K
## 3 0 0 40 NorthAmerica <=50K
## 4 0 0 40 NorthAmerica <=50K
## 5 0 0 40 CentrAmerica <=50K
## 6 0 0 40 NorthAmerica <=50K
## 7 0 0 16 CentrAmerica <=50K
## 8 0 0 45 NorthAmerica >50K
## 9 14084 0 50 NorthAmerica >50K
## 10 5178 0 40 NorthAmerica >50K
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## [ reached 'max' / getOption("max.print") -- omitted 26680 rows ]adult_dum <- adult
# one-hot-encode income
adult_dum$income <- ifelse(adult_dum$income == "<=50K", 0, 1)
# Loop through columns
for (col in names(adult_dum)) {
if (is.factor(adult_dum[[col]])) { # Check if column is a factor
levels <- levels(adult_dum[[col]]) # Get levels of the factor
adult_dum[[col]] <- as.numeric(factor(adult_dum[[col]], levels = levels)) # Convert factor to numeric based on levels
names(adult_dum)[names(adult_dum) == col] <- paste0(col, "_num") # Rename the column
}
}
adult_dum## age workclass_num similar_pop_count education_level_num years_education
## 1 39 8 77516 8 13
## 2 50 7 83311 8 13
## 3 38 5 215646 2 9
## 4 53 5 234721 2 7
## 5 28 5 338409 8 13
## 6 37 5 284582 3 14
## 7 49 5 160187 2 5
## 8 52 7 209642 2 9
## 9 31 5 45781 3 14
## 10 42 5 159449 8 13
## 11 37 5 280464 1 10
## 12 30 8 141297 8 13
## 13 23 5 122272 8 13
## 14 32 5 205019 1 12
## 15 40 5 121772 1 11
## 16 34 5 245487 7 4
## 17 25 7 176756 2 9
## 18 32 5 186824 2 9
## 19 38 5 28887 2 7
## 20 43 7 292175 3 14
## 21 40 5 193524 5 16
## 22 54 5 302146 2 9
## 23 35 2 76845 2 5
## 24 43 5 117037 2 7
## 25 59 5 109015 2 9
## 26 56 3 216851 8 13
## 27 19 5 168294 2 9
## 28 54 1 180211 1 10
## 29 39 5 367260 2 9
## 30 49 5 193366 2 9
## 31 23 3 190709 1 12
## 32 20 5 266015 1 10
## 33 45 5 386940 8 13
## 34 30 2 59951 1 10
## 35 22 8 311512 1 10
## 36 48 5 242406 2 7
## 37 21 5 197200 1 10
## 38 19 5 544091 2 9
## 39 31 5 84154 1 10
## 40 48 7 265477 1 12
## 41 31 5 507875 2 5
## 42 53 7 88506 8 13
## 43 24 5 172987 8 13
## 44 49 5 94638 2 9
## 45 25 5 289980 2 9
## 46 57 2 337895 8 13
## 47 53 5 144361 2 9
## 48 44 5 128354 3 14
## 49 41 8 101603 1 11
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## 51 25 5 32275 1 10
## 52 18 5 226956 2 9
## 53 47 5 51835 8 15
## 54 50 2 251585 8 13
## 55 47 6 109832 2 9
## 56 43 5 237993 1 10
## 57 46 5 216666 6 3
## 58 35 5 56352 1 11
## 59 41 5 147372 2 9
## 60 30 5 188146 2 9
## 61 30 5 59496 8 13
## 62 32 1 293936 7 4
## 63 48 5 149640 2 9
## 64 42 5 116632 5 16
## 65 29 5 105598 1 10
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## 132 24 5 43323 2 9
## 133 38 7 120985 2 9
## 134 37 5 254202 8 13
## 135 46 5 146195 1 12
## 136 38 2 125933 3 14
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## 273 50 7 30653 3 14
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## 281 22 5 34918 8 13
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## 5637 42 5 236110 6 3
## 5638 19 5 63363 1 10
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## 5640 37 5 126569 3 14
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## 5642 40 5 115161 2 9
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## 5644 38 5 256864 2 9
## 5645 48 5 265083 2 6
## 5646 34 5 249948 1 10
## 5647 46 2 31141 1 10
## 5648 30 5 164190 2 9
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## 5652 62 5 122246 1 10
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## 5655 25 5 189590 8 13
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## 5659 38 5 169926 1 12
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## 5662 19 5 93604 7 4
## 5663 31 5 86958 2 5
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## 5665 33 5 192644 2 9
## 5666 72 5 284080 6 2
## 5667 54 5 43269 2 9
## 5668 30 5 190040 8 13
## 5669 51 5 306108 5 16
## 5670 30 5 220148 2 9
## 5671 30 5 381645 8 13
## 5672 32 5 216361 1 10
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## 5695 44 5 182616 1 11
## 5696 63 5 339473 1 10
## 5697 31 3 381153 1 10
## 5698 51 5 300816 8 13
## 5699 51 5 240988 2 7
## 5700 23 5 149224 2 9
## 5701 49 5 168216 7 4
## 5702 56 5 286487 2 9
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## 5704 39 7 109766 8 15
## 5705 30 7 188798 2 9
## 5706 43 7 240170 8 13
## 5707 31 5 459465 1 10
## 5708 44 3 162506 3 14
## 5709 43 7 145441 8 15
## 5710 37 2 129573 5 16
## 5711 41 5 27444 1 10
## 5712 43 5 195258 2 9
## 5713 47 8 55272 1 10
## 5714 38 7 164526 8 15
## 5715 46 5 27802 1 11
## 5716 19 8 165289 1 10
## 5717 19 5 274657 6 3
## 5718 24 5 317175 8 13
## 5719 39 6 163237 1 10
## 5720 37 5 170408 1 11
## 5721 28 1 55950 8 13
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## 5723 27 5 366066 1 12
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## 5733 22 5 117789 1 10
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## 5741 31 5 273818 1 10
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## 5743 42 5 196001 2 9
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## 5758 57 7 102058 3 14
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## 5761 21 5 117606 1 10
## 5762 37 5 170718 2 9
## 5763 42 5 413297 1 10
## 5764 20 5 190457 2 9
## 5765 54 5 88278 5 16
## 5766 32 3 217296 2 9
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## 5783 23 5 133239 1 11
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## 5791 35 5 161141 2 9
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## 5794 43 3 233865 2 9
## 5795 51 5 163052 2 9
## 5796 35 5 348690 8 13
## 5797 47 2 34845 8 13
## 5798 22 5 206861 1 10
## 5799 49 6 349230 1 11
## 5800 20 5 130840 2 9
## 5801 19 5 415354 2 6
## 5802 27 5 132191 2 9
## 5803 42 5 202466 1 12
## 5804 27 1 224421 1 10
## 5805 23 7 236804 1 10
## 5806 20 5 107658 1 10
## 5807 47 5 102771 2 9
## 5808 17 5 221403 2 8
## 5809 76 1 211574 2 6
## 5810 39 5 52645 1 10
## 5811 25 5 276310 2 5
## 5812 31 5 134613 1 11
## 5813 44 5 215479 2 9
## 5814 53 5 266529 1 11
## 5815 34 5 265807 1 10
## 5816 45 7 67716 1 10
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## 5819 36 5 176544 1 10
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## 5821 37 7 282461 1 10
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## 5823 35 5 99357 3 14
## 5824 38 7 414991 2 9
## 5825 65 6 338316 1 10
## 5826 49 7 59612 2 6
## 5827 24 5 220426 2 9
## 5828 54 5 115912 1 10
## 5829 17 5 27032 2 6
## 5830 19 5 170720 2 8
## 5831 60 5 183162 2 9
## 5832 46 5 192360 8 13
## 5833 78 1 165694 3 14
## 5834 26 5 128553 1 10
## 5835 58 5 209423 6 2
## 5836 37 7 121510 2 9
## 5837 41 5 93793 3 14
## 5838 30 5 133602 8 13
## 5839 19 5 391329 1 10
## 5840 48 5 96359 8 13
## 5841 22 5 203894 1 10
## 5842 50 5 196193 3 14
## 5843 25 5 195994 6 2
## 5844 18 5 50879 2 6
## 5845 21 5 186849 2 9
## 5846 47 5 201127 1 10
## 5847 33 5 110998 2 9
## 5848 39 5 190466 2 9
## 5849 67 7 173935 8 13
## 5850 19 5 167140 1 10
## 5851 18 5 110230 2 6
## 5852 36 5 287658 2 9
## 5853 23 5 224954 1 10
## 5854 25 1 394820 1 10
## 5855 40 5 37618 1 10
## 5856 73 7 29306 2 9
## 5857 35 5 37314 1 10
## 5858 31 5 420749 1 12
## 5859 20 5 482732 2 6
## 5860 20 5 206215 2 9
## 5861 27 5 101364 2 9
## 5862 66 6 185369 2 6
## 5863 66 5 216856 3 14
## 5864 64 5 256019 3 14
## 5865 48 5 348144 1 10
## 5866 24 5 190293 8 13
## 5867 51 7 25932 2 9
## 5868 25 5 176729 8 13
## 5869 33 5 166961 2 7
## 5870 50 5 86373 2 9
## 5871 51 5 320513 7 4
## 5872 34 8 190290 8 13
## 5873 41 3 111891 7 4
## 5874 30 7 45796 1 10
## 5875 59 5 108496 1 10
## 5876 41 7 120539 1 10
## 5877 36 7 164526 3 14
## 5878 37 5 323155 6 2
## 5879 28 5 65389 2 9
## 5880 19 5 414871 2 9
## 5881 28 5 161607 1 10
## 5882 62 5 224953 2 6
## marital_status_num occupation_num household_role_num race_num gender_num
## 1 3 2 3 2 2
## 2 2 3 1 2 2
## 3 1 4 3 2 2
## 4 2 4 1 3 2
## 5 2 3 2 3 1
## 6 2 3 2 2 1
## 7 2 4 3 3 1
## 8 2 3 1 2 2
## 9 3 3 3 2 1
## 10 2 3 1 2 2
## 11 2 3 1 3 2
## 12 2 3 1 5 2
## 13 3 2 6 2 1
## 14 3 3 3 3 2
## 15 2 5 1 5 2
## 16 2 5 1 4 2
## 17 3 1 6 2 2
## 18 3 5 4 2 2
## 19 2 3 1 2 2
## 20 1 3 4 2 1
## 21 2 3 1 2 2
## 22 1 4 4 3 1
## 23 2 1 1 3 2
## 24 2 5 1 2 2
## 25 1 3 4 2 1
## 26 2 3 1 2 2
## 27 3 5 6 2 2
## 28 2 6 1 5 2
## 29 1 3 3 2 2
## 30 2 5 1 2 2
## 31 3 3 3 2 2
## 32 3 3 6 3 2
## 33 1 3 6 2 2
## 34 2 2 6 2 2
## 35 2 4 1 3 2
## 36 3 5 4 2 2
## 37 3 5 6 2 2
## 38 2 2 2 2 1
## 39 2 3 1 2 2
## 40 2 3 1 2 2
## 41 2 5 1 2 2
## 42 2 3 1 2 2
## 43 2 3 1 2 2
## 44 1 2 4 2 1
## 45 3 4 3 2 2
## 46 2 3 1 3 2
## 47 2 5 1 2 2
## 48 1 3 4 2 1
## 49 2 5 1 2 2
## 50 3 3 3 2 2
## 51 2 3 2 1 1
## 52 3 4 6 2 1
## 53 2 3 2 2 1
## 54 1 3 3 2 2
## 55 1 3 3 2 2
## 56 2 3 1 2 2
## 57 2 5 1 2 2
## 58 2 4 1 2 2
## 59 2 2 1 2 2
## 60 2 5 1 2 2
## 61 2 3 1 2 2
## 62 2 6 3 2 2
## 63 2 5 1 2 2
## 64 2 3 1 2 2
## 65 1 3 3 2 2
## 66 2 5 1 2 2
## 67 1 2 3 2 1
## 68 2 2 2 2 1
## 69 2 3 1 2 2
## 70 3 6 6 2 2
## 71 3 3 6 2 2
## 72 1 3 6 3 1
## 73 2 3 1 2 2
## 74 3 5 3 2 2
## 75 2 3 5 2 2
## 76 3 4 6 2 2
## 77 2 2 1 2 2
## 78 2 6 1 2 2
## 79 3 4 6 2 1
## 80 2 1 1 2 2
## 81 3 3 3 2 2
## 82 2 4 1 2 2
## 83 2 4 2 2 1
## 84 2 3 1 2 2
## 85 1 5 3 2 1
## 86 1 3 6 2 1
## 87 2 3 1 2 2
## 88 2 3 1 2 2
## 89 3 3 3 2 2
## 90 3 3 3 2 1
## 91 2 3 1 2 2
## 92 1 5 4 2 1
## 93 1 5 4 3 1
## 94 2 3 2 5 1
## 95 2 3 1 2 2
## 96 3 4 3 2 2
## 97 2 3 1 2 2
## 98 2 3 1 2 2
## 99 1 3 4 2 1
## 100 3 4 6 3 2
## 101 2 3 1 2 2
## 102 2 3 1 2 2
## 103 3 3 3 2 1
## 104 3 3 6 2 1
## 105 3 5 3 2 2
## 106 2 5 1 2 2
## 107 3 6 6 2 1
## 108 3 4 6 2 2
## 109 3 2 3 2 1
## 110 2 4 1 2 2
## 111 3 3 5 2 2
## 112 2 3 1 2 2
## 113 2 4 1 2 2
## 114 2 3 2 2 1
## 115 3 5 6 2 1
## 116 2 4 1 2 2
## 117 2 3 1 2 2
## 118 2 5 1 3 2
## 119 2 3 1 2 2
## 120 3 5 3 2 2
## 121 3 4 6 2 1
## 122 2 3 1 2 2
## 123 3 3 6 3 2
## 124 2 3 1 2 2
## 125 3 4 6 3 2
## 126 2 3 2 2 1
## 127 3 2 6 2 1
## 128 2 2 1 2 2
## 129 2 6 1 2 2
## 130 1 5 3 2 2
## 131 3 3 3 2 1
## 132 3 4 3 2 1
## 133 2 5 1 2 2
## 134 2 3 1 2 2
## 135 1 3 3 3 1
## 136 2 3 1 2 2
## 137 2 5 1 2 2
## 138 2 2 2 2 1
## 139 3 3 6 2 2
## 140 2 5 1 5 2
## 141 2 5 1 2 2
## 142 3 3 6 2 1
## 143 3 4 6 2 2
## 144 3 3 3 2 1
## 145 3 3 5 2 2
## 146 3 5 4 3 1
## 147 2 4 1 2 2
## 148 4 4 4 2 1
## 149 2 5 1 2 2
## 150 1 6 3 2 1
## 151 1 2 4 2 1
## 152 3 2 3 2 1
## 153 3 3 5 5 1
## 154 3 5 6 2 2
## 155 1 6 3 2 2
## 156 2 5 1 2 2
## 157 3 4 3 2 2
## 158 1 3 4 3 2
## 159 1 5 3 2 2
## 160 1 4 5 3 2
## 161 1 6 3 2 1
## 162 3 4 4 2 1
## 163 1 3 4 5 1
## 164 3 3 3 5 1
## 165 2 3 1 2 2
## 166 1 4 4 2 1
## 167 3 3 3 2 2
## 168 4 3 4 2 2
## 169 3 4 6 2 2
## 170 4 3 3 2 1
## 171 3 4 3 2 2
## 172 2 3 1 2 2
## 173 2 3 1 2 2
## 174 3 5 3 2 2
## 175 2 3 1 2 2
## 176 2 3 4 2 1
## 177 3 5 6 3 2
## 178 3 4 3 3 2
## 179 3 4 6 2 2
## 180 2 3 1 2 2
## 181 2 3 1 3 2
## 182 1 2 3 2 1
## 183 3 5 3 3 2
## 184 2 5 1 2 2
## 185 2 2 1 2 2
## 186 2 2 1 3 2
## 187 1 3 3 2 1
## 188 1 6 3 2 1
## 189 2 3 2 2 1
## 190 2 3 1 2 2
## 191 1 5 3 3 1
## 192 2 5 1 2 2
## 193 2 5 1 3 2
## 194 2 3 1 2 2
## 195 2 3 1 2 2
## 196 2 5 5 2 2
## 197 1 5 3 2 2
## 198 3 3 3 2 1
## 199 2 3 5 2 2
## 200 3 3 3 2 2
## 201 3 3 3 2 1
## 202 3 6 6 2 2
## 203 2 3 1 2 2
## 204 3 3 3 2 1
## 205 1 2 4 2 1
## 206 3 3 6 2 1
## 207 2 4 1 3 2
## 208 1 5 3 2 2
## 209 2 3 2 2 1
## 210 3 3 6 2 1
## 211 2 5 1 3 2
## 212 2 5 1 2 2
## 213 3 3 3 4 1
## 214 2 5 1 5 2
## 215 2 5 1 2 2
## 216 3 3 3 3 2
## 217 1 3 3 2 1
## 218 2 3 1 2 2
## 219 2 3 1 2 2
## 220 3 5 3 2 2
## 221 2 4 1 2 2
## 222 1 6 3 2 2
## 223 3 4 3 3 2
## 224 2 2 1 3 2
## 225 3 5 3 2 1
## 226 3 3 6 2 1
## 227 1 6 3 4 1
## 228 2 1 1 2 2
## 229 4 2 3 2 1
## 230 1 2 3 2 1
## 231 1 5 3 3 2
## 232 1 3 4 3 1
## 233 3 2 3 2 2
## 234 1 5 5 1 1
## 235 3 3 3 2 2
## 236 2 4 1 2 2
## 237 3 2 3 2 1
## 238 2 3 1 2 2
## 239 2 3 1 2 2
## 240 3 5 6 2 2
## 241 2 3 1 2 2
## 242 2 4 2 2 1
## 243 2 3 1 2 2
## 244 3 6 6 2 2
## 245 1 5 3 2 2
## 246 2 2 1 2 2
## 247 2 2 1 2 2
## 248 3 4 3 2 1
## 249 2 3 1 2 2
## 250 2 2 1 2 2
## 251 2 3 2 2 1
## 252 2 2 2 3 1
## 253 4 4 4 3 1
## 254 3 2 6 2 2
## 255 3 5 3 2 2
## 256 2 1 1 5 2
## 257 3 4 6 2 1
## 258 1 4 3 2 1
## 259 4 3 3 2 1
## 260 1 5 3 2 2
## 261 1 3 6 2 1
## 262 2 3 1 2 2
## 263 3 4 6 2 2
## 264 2 3 1 2 2
## 265 2 5 1 2 2
## 266 2 3 1 5 2
## 267 3 6 6 2 2
## 268 2 3 1 2 2
## 269 3 3 3 2 1
## 270 2 5 2 2 1
## 271 2 3 1 2 2
## 272 3 4 6 2 2
## 273 2 1 1 2 2
## 274 3 3 6 2 2
## 275 1 3 6 2 1
## 276 2 4 1 3 2
## 277 2 5 1 2 2
## 278 3 3 3 2 1
## 279 3 3 6 2 1
## 280 2 5 2 2 1
## 281 3 3 3 2 1
## 282 2 5 1 2 2
## 283 1 3 4 3 1
## 284 1 3 3 2 1
## 285 3 4 6 2 2
## 286 2 3 1 2 2
## 287 2 3 1 2 2
## 288 1 4 3 2 2
## 289 3 3 3 2 1
## 290 2 5 1 2 2
## 291 2 3 1 2 2
## 292 1 3 3 2 1
## 293 3 3 3 2 2
## 294 3 3 6 2 2
## 295 3 3 4 3 1
## 296 2 5 1 2 2
## 297 1 3 3 2 1
## 298 2 6 2 5 1
## 299 3 5 6 2 2
## 300 1 3 3 2 2
## 301 2 2 1 2 2
## 302 3 4 3 5 1
## 303 2 5 1 2 2
## 304 2 3 1 2 2
## 305 2 2 2 5 1
## 306 1 2 3 2 1
## 307 3 3 3 2 2
## 308 3 3 3 2 1
## 309 2 2 1 2 2
## 310 2 1 1 2 2
## 311 3 3 3 2 2
## 312 2 3 1 5 2
## 313 3 6 6 3 1
## 314 1 3 3 3 1
## 315 3 3 6 2 2
## 316 3 5 3 2 2
## 317 2 3 1 2 2
## 318 2 5 5 2 1
## 319 3 3 3 2 1
## 320 4 4 3 2 1
## 321 2 3 1 2 2
## 322 2 5 1 2 2
## 323 1 5 3 2 2
## 324 3 3 3 2 1
## 325 2 5 1 2 2
## 326 2 1 1 2 2
## 327 3 6 6 3 1
## 328 2 3 2 2 1
## 329 2 4 1 2 2
## 330 3 5 3 2 2
## 331 3 5 3 3 2
## 332 3 2 3 2 2
## 333 3 4 3 2 2
## 334 2 2 1 2 2
## 335 2 5 1 2 2
## 336 3 4 5 2 2
## 337 3 5 5 2 2
## 338 3 3 3 2 2
## 339 3 3 4 3 2
## 340 2 4 1 3 2
## 341 3 3 3 2 2
## 342 2 5 1 2 2
## 343 2 3 1 2 2
## 344 3 1 3 2 2
## 345 3 3 6 3 1
## 346 3 3 3 2 2
## 347 2 6 1 2 2
## 348 1 6 4 2 1
## 349 2 4 3 2 2
## 350 3 3 6 2 2
## 351 2 3 1 2 2
## 352 2 4 1 2 2
## 353 2 3 1 2 2
## 354 2 5 1 2 2
## 355 2 6 1 2 2
## 356 2 2 1 2 2
## 357 4 4 4 1 2
## 358 2 3 1 2 2
## 359 2 2 2 2 1
## 360 2 5 1 2 2
## 361 2 4 2 2 1
## 362 2 3 1 2 2
## 363 2 5 1 2 2
## 364 1 3 3 2 2
## 365 2 3 3 2 2
## 366 2 5 1 2 2
## 367 2 3 1 2 2
## 368 3 3 3 2 1
## 369 1 3 3 2 1
## 370 1 3 4 2 1
## 371 3 2 4 2 1
## 372 3 4 6 2 2
## 373 2 5 1 2 2
## 374 2 5 1 3 2
## 375 3 5 3 3 2
## 376 3 2 6 2 1
## 377 2 5 2 2 1
## 378 3 3 3 2 2
## 379 1 3 3 2 2
## 380 2 3 1 2 2
## 381 1 5 3 2 2
## 382 1 5 3 2 1
## 383 2 5 1 2 2
## 384 1 3 3 2 1
## 385 2 5 2 2 1
## 386 3 4 3 2 1
## 387 1 5 3 2 2
## 388 2 2 2 2 1
## 389 3 2 6 2 1
## 390 3 4 5 2 1
## 391 2 4 2 2 1
## 392 3 5 3 2 2
## 393 1 5 3 2 2
## 394 2 3 1 2 2
## 395 3 4 3 2 1
## 396 2 3 1 2 2
## 397 1 4 4 2 1
## 398 3 6 5 2 1
## 399 2 3 1 2 2
## 400 2 3 1 2 2
## 401 3 4 6 2 2
## 402 3 4 3 2 1
## 403 2 3 1 2 2
## 404 1 3 3 2 1
## 405 2 4 1 1 2
## 406 2 3 1 2 2
## 407 2 2 1 2 2
## 408 2 5 1 2 2
## 409 2 6 2 2 1
## 410 2 3 1 2 2
## 411 1 4 3 3 1
## 412 3 4 4 3 1
## 413 1 3 4 2 1
## 414 2 3 1 2 2
## 415 2 5 1 2 2
## 416 1 5 3 2 2
## 417 2 5 1 2 2
## 418 3 4 6 2 1
## 419 1 5 3 3 2
## 420 1 5 4 2 1
## 421 2 3 1 2 2
## 422 3 4 6 2 2
## 423 2 3 2 2 1
## 424 1 1 3 2 2
## 425 3 5 4 2 1
## 426 2 5 1 2 2
## 427 3 3 3 2 2
## 428 3 4 3 2 2
## 429 2 3 1 2 2
## 430 2 3 1 2 2
## 431 4 6 3 2 2
## 432 3 6 6 2 2
## 433 2 3 3 2 1
## 434 2 4 2 2 1
## 435 3 5 6 5 2
## 436 2 3 1 2 2
## 437 3 1 3 3 2
## 438 3 3 3 2 1
## 439 2 2 1 2 2
## 440 2 2 2 2 1
## 441 3 3 3 2 2
## 442 1 2 3 2 1
## 443 3 4 6 2 2
## 444 2 3 1 2 2
## 445 3 5 5 2 2
## 446 3 3 3 2 2
## 447 4 3 4 3 1
## 448 2 5 1 4 2
## 449 2 5 1 2 2
## 450 3 6 6 2 1
## 451 3 5 6 3 1
## 452 2 5 1 2 2
## 453 2 5 1 2 2
## 454 2 3 1 3 2
## 455 2 3 2 2 1
## 456 2 5 1 5 2
## 457 2 3 2 2 1
## 458 2 5 1 3 2
## 459 2 5 1 2 2
## 460 2 6 1 2 2
## 461 3 1 6 2 2
## 462 2 5 1 3 2
## 463 2 3 1 2 2
## 464 2 5 4 2 2
## 465 3 3 3 2 1
## 466 2 2 2 2 1
## 467 1 5 4 2 2
## 468 2 5 1 2 2
## 469 2 3 1 2 2
## 470 2 3 2 2 1
## 471 3 3 4 2 2
## 472 3 6 6 2 2
## 473 3 5 3 2 2
## 474 3 5 3 2 2
## 475 1 4 3 2 1
## 476 3 5 3 2 1
## 477 3 4 3 2 2
## 478 3 4 6 2 2
## 479 3 4 3 2 2
## 480 3 4 4 2 1
## 481 2 5 1 2 2
## 482 2 5 1 2 2
## 483 3 3 3 2 2
## 484 2 5 4 2 1
## 485 3 6 5 3 2
## 486 2 2 6 3 1
## 487 2 6 1 2 2
## 488 2 5 1 2 2
## 489 1 3 4 2 1
## 490 2 3 1 2 2
## 491 3 4 3 2 1
## 492 1 2 3 2 2
## 493 3 3 3 2 1
## 494 1 3 4 2 1
## 495 3 5 6 2 2
## 496 1 2 4 2 1
## 497 3 5 6 2 2
## 498 3 4 6 2 2
## 499 3 3 3 2 1
## 500 2 6 1 2 2
## 501 3 1 3 2 2
## 502 2 5 1 2 2
## 503 2 2 1 2 2
## 504 1 4 4 2 1
## 505 3 3 3 2 1
## 506 3 3 3 3 2
## 507 3 3 6 2 2
## 508 2 3 1 2 2
## 509 1 2 4 3 1
## 510 1 4 4 2 1
## 511 2 4 4 2 2
## 512 4 6 4 3 1
## 513 3 5 3 2 2
## 514 2 3 1 2 2
## 515 1 5 3 2 2
## 516 4 6 4 2 1
## 517 3 2 3 4 1
## 518 3 6 6 2 2
## 519 3 6 4 2 1
## 520 2 3 1 2 2
## 521 2 3 1 2 2
## 522 2 3 1 2 2
## 523 1 4 3 3 2
## 524 2 3 1 2 2
## 525 1 5 4 2 2
## 526 2 3 2 2 1
## 527 2 3 1 2 2
## 528 1 2 3 2 1
## 529 2 5 1 2 2
## 530 2 3 1 2 2
## 531 1 5 4 1 1
## 532 3 5 3 2 2
## 533 2 4 1 3 2
## 534 2 3 2 2 1
## 535 2 3 1 2 2
## 536 2 3 1 2 2
## 537 4 4 3 3 1
## 538 1 5 3 2 2
## 539 3 1 3 2 1
## 540 3 6 4 3 1
## 541 2 5 1 2 2
## 542 3 3 6 2 2
## 543 3 4 4 2 1
## 544 3 5 3 2 2
## 545 2 3 1 2 2
## 546 3 4 3 2 1
## 547 2 4 2 2 1
## 548 2 3 1 2 2
## 549 3 4 6 2 2
## 550 3 5 4 2 2
## 551 1 3 3 2 2
## 552 1 4 4 2 1
## 553 4 2 4 2 1
## 554 2 5 2 3 1
## 555 1 5 4 3 1
## 556 3 5 3 2 1
## 557 2 5 1 2 2
## 558 1 2 4 2 1
## 559 2 3 2 2 1
## 560 2 4 1 2 2
## 561 3 5 3 2 2
## 562 2 3 1 2 2
## 563 2 3 1 2 2
## 564 3 4 4 3 1
## 565 2 3 1 2 2
## 566 3 4 6 4 2
## 567 2 3 1 2 2
## 568 1 4 4 2 1
## 569 1 5 3 2 2
## 570 1 3 4 2 2
## 571 3 5 6 2 2
## 572 2 3 1 2 2
## 573 3 3 6 2 2
## 574 2 5 1 2 2
## 575 3 3 3 2 1
## 576 2 3 2 2 2
## 577 3 6 6 2 2
## 578 1 3 4 2 1
## 579 3 3 3 2 1
## 580 2 5 1 2 2
## 581 2 6 1 2 2
## 582 2 3 1 2 2
## 583 2 5 1 2 2
## 584 2 5 1 2 2
## 585 3 3 3 2 2
## 586 3 3 6 2 1
## 587 3 2 3 2 2
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## 5515 3 3 3 2 1
## 5516 3 2 6 2 1
## 5517 2 5 1 2 2
## 5518 2 5 1 2 2
## 5519 2 3 1 2 2
## 5520 3 3 3 5 1
## 5521 3 4 3 3 1
## 5522 2 5 1 2 2
## 5523 3 5 4 2 2
## 5524 2 2 2 2 1
## 5525 3 5 5 2 2
## 5526 3 3 3 5 1
## 5527 3 6 6 2 2
## 5528 1 4 4 2 1
## 5529 1 3 6 2 1
## 5530 2 6 1 2 2
## 5531 2 3 2 3 1
## 5532 2 5 1 2 2
## 5533 1 3 3 2 1
## 5534 1 3 4 2 1
## 5535 1 4 4 2 1
## 5536 3 4 6 2 2
## 5537 2 5 1 3 2
## 5538 2 3 1 2 2
## 5539 2 3 1 2 2
## 5540 3 3 3 2 2
## 5541 2 4 1 2 2
## 5542 3 5 3 2 2
## 5543 2 3 2 2 1
## 5544 2 3 1 2 2
## 5545 1 3 3 2 2
## 5546 3 2 6 2 2
## 5547 3 4 3 3 1
## 5548 2 3 1 2 2
## 5549 1 6 3 2 2
## 5550 2 3 1 2 2
## 5551 2 5 1 2 2
## 5552 1 3 3 3 2
## 5553 2 3 1 2 2
## 5554 3 3 6 2 2
## 5555 2 5 1 2 2
## 5556 2 3 1 2 2
## 5557 3 3 6 2 1
## 5558 1 4 4 2 1
## 5559 2 3 1 2 2
## 5560 2 2 3 2 1
## 5561 2 3 1 5 2
## 5562 4 6 3 2 1
## 5563 2 3 1 2 2
## 5564 1 5 3 2 2
## 5565 2 3 1 2 2
## 5566 3 6 5 2 1
## 5567 2 3 1 2 2
## 5568 1 5 3 2 2
## 5569 2 5 1 3 2
## 5570 3 3 3 2 1
## 5571 2 5 1 2 2
## 5572 1 2 5 2 1
## 5573 1 3 3 2 2
## 5574 2 5 1 2 2
## 5575 3 3 3 2 2
## 5576 2 1 1 2 2
## 5577 3 5 3 2 2
## 5578 2 5 1 2 2
## 5579 3 4 3 2 1
## 5580 3 5 3 2 1
## 5581 2 3 1 2 2
## 5582 2 5 1 2 2
## 5583 2 3 1 2 2
## 5584 3 5 6 2 2
## 5585 2 3 1 2 2
## 5586 1 2 4 3 1
## 5587 2 3 1 1 2
## 5588 2 4 1 2 2
## 5589 2 3 1 2 2
## 5590 2 3 1 2 2
## 5591 4 6 3 2 1
## 5592 3 3 3 2 1
## 5593 3 3 5 5 2
## 5594 2 3 1 2 2
## 5595 3 4 5 3 1
## 5596 2 5 1 2 2
## 5597 2 3 1 2 2
## 5598 3 5 5 2 2
## 5599 3 4 6 2 2
## 5600 3 3 3 2 2
## 5601 2 3 1 2 2
## 5602 2 3 1 2 2
## 5603 2 3 1 2 2
## 5604 3 3 6 5 2
## 5605 3 4 5 2 2
## 5606 1 3 4 3 1
## 5607 2 3 1 2 2
## 5608 3 5 3 3 2
## 5609 1 5 4 2 2
## 5610 1 3 4 2 1
## 5611 2 3 1 2 2
## 5612 2 3 6 2 1
## 5613 2 2 1 2 2
## 5614 3 3 6 3 2
## 5615 3 5 3 2 2
## 5616 1 1 3 2 1
## 5617 3 4 6 2 1
## 5618 3 3 3 5 1
## 5619 3 3 3 2 2
## 5620 2 4 1 2 2
## 5621 2 5 1 2 2
## 5622 3 5 6 2 2
## 5623 1 4 4 2 1
## 5624 2 6 1 1 2
## 5625 2 2 2 4 1
## 5626 2 3 1 2 2
## 5627 2 4 3 3 1
## 5628 1 2 4 2 1
## 5629 2 1 1 2 2
## 5630 2 5 5 5 2
## 5631 1 3 3 2 1
## 5632 2 4 1 2 2
## 5633 2 6 2 2 1
## 5634 3 2 6 2 2
## 5635 2 3 1 2 2
## 5636 1 3 4 2 2
## 5637 2 5 3 2 2
## 5638 3 3 6 2 1
## 5639 3 5 6 2 2
## 5640 2 3 1 2 2
## 5641 2 4 1 3 2
## 5642 2 5 1 2 2
## 5643 2 3 1 2 2
## 5644 2 3 1 2 2
## 5645 1 3 3 2 1
## 5646 1 4 3 2 1
## 5647 1 5 3 2 2
## 5648 3 2 3 2 2
## 5649 1 3 3 2 2
## 5650 2 3 1 2 2
## 5651 3 5 6 2 2
## 5652 3 5 3 2 1
## 5653 2 6 1 2 2
## 5654 2 4 1 2 2
## 5655 3 3 3 2 2
## 5656 3 3 3 3 2
## 5657 3 4 6 2 1
## 5658 2 5 1 2 2
## 5659 2 3 2 2 1
## 5660 3 3 3 2 2
## 5661 2 5 1 2 2
## 5662 3 5 6 2 2
## 5663 2 3 2 2 1
## 5664 1 5 3 1 2
## 5665 1 4 4 2 2
## 5666 1 4 3 3 2
## 5667 2 5 1 2 2
## 5668 3 5 3 2 1
## 5669 3 3 3 2 2
## 5670 2 5 1 2 2
## 5671 3 3 3 2 2
## 5672 2 3 1 3 2
## 5673 3 3 3 2 2
## 5674 2 2 1 2 2
## 5675 1 2 6 2 1
## 5676 1 3 3 2 1
## 5677 3 4 5 2 1
## 5678 3 3 3 2 2
## 5679 2 2 4 2 1
## 5680 2 6 1 2 2
## 5681 2 3 1 2 2
## 5682 2 3 1 2 2
## 5683 2 3 1 2 2
## 5684 1 3 3 2 1
## 5685 3 5 6 2 2
## 5686 2 3 1 2 2
## 5687 4 2 3 2 1
## 5688 2 6 1 2 2
## 5689 2 2 2 2 1
## 5690 3 4 6 2 1
## 5691 2 5 1 2 2
## 5692 1 3 3 2 2
## 5693 3 4 6 2 1
## 5694 1 2 4 2 1
## 5695 2 3 1 2 2
## 5696 1 3 3 2 1
## 5697 2 3 1 2 2
## 5698 3 2 4 2 2
## 5699 2 5 1 5 2
## 5700 3 3 3 2 2
## 5701 2 5 1 2 2
## 5702 2 5 1 2 2
## 5703 2 5 1 2 2
## 5704 2 3 1 2 2
## 5705 1 4 4 2 1
## 5706 2 3 1 2 2
## 5707 3 5 3 2 2
## 5708 2 3 1 2 2
## 5709 2 3 1 2 2
## 5710 2 3 1 2 2
## 5711 2 2 1 2 2
## 5712 2 2 1 2 2
## 5713 2 5 1 2 2
## 5714 3 3 3 2 2
## 5715 2 5 1 2 2
## 5716 3 4 6 2 1
## 5717 3 4 3 2 2
## 5718 2 3 1 2 2
## 5719 2 1 1 2 2
## 5720 1 5 3 2 1
## 5721 3 6 6 3 1
## 5722 1 5 4 2 2
## 5723 3 3 6 2 1
## 5724 3 2 5 2 1
## 5725 3 4 6 2 2
## 5726 2 3 2 2 1
## 5727 1 5 6 2 2
## 5728 1 5 3 2 2
## 5729 2 3 1 2 2
## 5730 1 3 4 2 1
## 5731 3 3 3 2 2
## 5732 2 3 6 2 2
## 5733 3 3 6 2 1
## 5734 2 3 1 2 2
## 5735 2 5 1 2 2
## 5736 3 4 6 2 2
## 5737 2 5 1 2 2
## 5738 2 5 1 3 2
## 5739 2 5 1 2 2
## 5740 3 4 6 2 2
## 5741 2 4 1 2 2
## 5742 3 5 6 2 2
## 5743 1 2 4 2 1
## 5744 2 5 1 2 2
## 5745 3 3 3 2 2
## 5746 2 5 1 2 2
## 5747 3 3 6 2 1
## 5748 2 3 1 2 2
## 5749 2 3 1 2 2
## 5750 2 5 1 2 2
## 5751 1 3 4 3 1
## 5752 2 5 1 2 2
## 5753 2 5 1 2 2
## 5754 2 6 2 2 1
## 5755 2 3 1 2 2
## 5756 2 4 1 2 2
## 5757 2 5 1 2 2
## 5758 2 3 1 2 2
## 5759 3 2 6 1 1
## 5760 1 3 5 2 1
## 5761 3 3 3 2 1
## 5762 2 3 1 2 2
## 5763 2 3 1 2 2
## 5764 3 4 6 2 1
## 5765 2 3 1 2 2
## 5766 2 5 2 2 1
## 5767 2 6 2 2 1
## 5768 2 5 1 2 2
## 5769 3 2 4 3 1
## 5770 2 5 1 2 2
## 5771 3 3 5 3 1
## 5772 2 3 1 5 2
## 5773 1 4 3 2 1
## 5774 2 5 1 2 2
## 5775 4 2 3 3 1
## 5776 1 3 3 4 1
## 5777 2 5 1 2 2
## 5778 2 2 1 2 2
## 5779 3 2 5 2 1
## 5780 3 3 3 3 2
## 5781 1 3 3 2 1
## 5782 3 2 6 2 1
## 5783 3 3 3 2 1
## 5784 3 3 6 2 1
## 5785 2 4 2 2 1
## 5786 3 3 6 2 1
## 5787 2 5 1 5 2
## 5788 1 2 3 2 1
## 5789 1 6 6 3 1
## 5790 1 4 4 2 1
## 5791 2 5 1 2 2
## 5792 2 5 1 2 2
## 5793 2 3 1 2 2
## 5794 2 3 1 2 2
## 5795 2 3 1 2 2
## 5796 2 5 1 2 2
## 5797 2 2 1 2 2
## 5798 3 2 6 2 2
## 5799 2 3 1 2 2
## 5800 3 5 6 2 2
## 5801 1 4 3 2 1
## 5802 3 5 3 2 2
## 5803 1 3 4 2 1
## 5804 1 6 6 2 2
## 5805 3 1 6 2 2
## 5806 3 3 3 2 1
## 5807 2 5 1 2 2
## 5808 3 4 6 3 2
## 5809 2 6 1 2 2
## 5810 1 3 4 2 1
## 5811 2 5 1 2 2
## 5812 2 3 2 3 1
## 5813 1 5 3 3 2
## 5814 2 3 1 2 2
## 5815 1 5 3 2 2
## 5816 2 3 1 2 2
## 5817 2 3 1 2 2
## 5818 2 3 1 2 2
## 5819 3 4 3 2 2
## 5820 4 4 4 2 1
## 5821 2 3 1 2 2
## 5822 2 5 1 2 2
## 5823 2 3 2 2 1
## 5824 2 5 1 2 2
## 5825 2 3 1 2 2
## 5826 1 1 4 2 2
## 5827 3 4 3 2 2
## 5828 2 5 1 2 2
## 5829 3 3 6 2 1
## 5830 3 4 6 2 2
## 5831 4 5 3 2 1
## 5832 2 3 1 2 2
## 5833 4 6 3 2 1
## 5834 3 3 6 3 1
## 5835 2 4 1 2 2
## 5836 1 3 4 2 2
## 5837 2 3 1 2 2
## 5838 1 3 3 2 2
## 5839 3 2 3 2 1
## 5840 2 3 1 2 2
## 5841 3 5 3 2 1
## 5842 2 3 5 2 2
## 5843 3 4 3 2 1
## 5844 3 4 6 2 2
## 5845 3 4 6 2 2
## 5846 2 5 1 2 2
## 5847 3 4 5 4 1
## 5848 1 5 6 2 2
## 5849 2 3 1 2 2
## 5850 3 4 6 2 2
## 5851 3 4 6 2 2
## 5852 3 2 6 3 2
## 5853 3 5 6 2 2
## 5854 1 6 4 2 1
## 5855 2 5 1 2 2
## 5856 2 1 1 2 2
## 5857 2 5 1 2 2
## 5858 2 5 1 2 2
## 5859 3 5 3 2 2
## 5860 3 2 6 2 1
## 5861 2 4 1 2 2
## 5862 2 3 1 2 2
## 5863 2 3 1 2 2
## 5864 3 3 3 2 1
## 5865 1 5 3 2 2
## 5866 3 3 3 2 1
## 5867 2 1 1 2 2
## 5868 2 5 1 2 2
## 5869 1 3 6 2 1
## 5870 2 5 1 2 2
## 5871 2 5 3 3 2
## 5872 2 3 1 2 2
## 5873 3 4 4 3 1
## 5874 2 5 1 2 2
## 5875 1 5 3 2 2
## 5876 2 5 1 2 2
## 5877 3 3 3 2 2
## 5878 2 1 1 2 2
## 5879 3 4 3 4 2
## 5880 3 5 6 2 2
## 5881 2 5 1 2 2
## 5882 2 5 1 3 2
## capital_gain capital_loss hours_per_week native_region_num income
## 1 2174 0 40 4 0
## 2 0 0 13 4 0
## 3 0 0 40 4 0
## 4 0 0 40 4 0
## 5 0 0 40 1 0
## 6 0 0 40 4 0
## 7 0 0 16 1 0
## 8 0 0 45 4 1
## 9 14084 0 50 4 1
## 10 5178 0 40 4 1
## 11 0 0 80 4 1
## 12 0 0 40 5 1
## 13 0 0 30 4 0
## 14 0 0 50 4 0
## 15 0 0 40 1 1
## 16 0 0 45 4 0
## 17 0 0 35 4 0
## 18 0 0 40 4 0
## 19 0 0 50 4 0
## 20 0 0 45 4 1
## 21 0 0 60 4 1
## 22 0 0 20 4 0
## 23 0 0 40 4 0
## 24 0 2042 40 4 0
## 25 0 0 40 4 0
## 26 0 0 40 4 1
## 27 0 0 40 4 0
## 28 0 0 60 6 1
## 29 0 0 80 4 0
## 30 0 0 40 4 0
## 31 0 0 52 4 0
## 32 0 0 44 4 0
## 33 0 1408 40 4 0
## 34 0 0 40 4 0
## 35 0 0 15 4 0
## 36 0 0 40 7 0
## 37 0 0 40 4 0
## 38 0 0 25 4 0
## 39 0 0 38 1 1
## 40 0 0 40 4 0
## 41 0 0 43 4 0
## 42 0 0 40 4 0
## 43 0 0 50 4 0
## 44 0 0 40 4 0
## 45 0 0 35 4 0
## 46 0 0 40 4 1
## 47 0 0 38 4 0
## 48 0 0 40 4 0
## 49 0 0 40 4 0
## 50 0 0 43 4 0
## 51 0 0 40 4 0
## 52 0 0 30 1 0
## 53 0 1902 60 1 1
## 54 0 0 55 4 1
## 55 0 0 60 4 0
## 56 0 0 40 4 1
## 57 0 0 40 4 0
## 58 0 0 40 7 0
## 59 0 0 48 4 0
## 60 5013 0 40 4 0
## 61 2407 0 40 4 0
## 62 0 0 40 1 0
## 63 0 0 40 4 0
## 64 0 0 45 4 1
## 65 0 0 58 4 0
## 66 0 0 40 4 0
## 67 0 0 40 4 0
## 68 0 0 40 4 1
## 69 0 0 50 4 1
## 70 0 0 40 4 0
## 71 0 0 32 4 0
## 72 0 0 40 4 0
## 73 0 0 70 4 1
## 74 0 0 40 4 0
## 75 0 0 20 4 0
## 76 0 0 40 4 0
## 77 0 0 40 4 0
## 78 0 0 2 4 0
## 79 0 0 22 4 0
## 80 0 0 40 4 0
## 81 0 0 30 4 0
## 82 0 0 40 1 0
## 83 0 0 40 4 0
## 84 0 0 48 4 0
## 85 14344 0 40 4 1
## 86 0 0 35 4 0
## 87 0 0 40 4 1
## 88 0 0 50 4 0
## 89 0 0 40 4 0
## 90 0 0 50 4 1
## 91 0 0 40 4 0
## 92 0 0 40 4 0
## 93 0 0 25 4 0
## 94 0 1573 35 1 0
## 95 0 0 40 4 1
## 96 0 0 50 4 0
## 97 0 1902 60 4 1
## 98 0 0 48 4 1
## 99 0 0 40 3 0
## 100 0 0 40 4 0
## 101 0 0 40 4 1
## 102 15024 0 60 4 1
## 103 0 0 50 4 0
## 104 0 0 40 4 0
## 105 0 0 40 4 0
## 106 7688 0 40 4 1
## 107 34095 0 32 4 0
## 108 0 0 40 4 0
## 109 0 0 40 4 0
## 110 0 0 40 4 0
## 111 0 0 40 4 0
## 112 0 0 40 4 1
## 113 0 1887 50 4 1
## 114 4064 0 25 4 0
## 115 0 0 40 4 0
## 116 0 0 50 4 0
## 117 0 0 50 4 0
## 118 0 0 40 4 1
## 119 0 0 56 4 0
## 120 0 0 41 4 0
## 121 0 0 40 4 0
## 122 0 0 60 4 0
## 123 0 0 40 3 0
## 124 0 0 50 4 1
## 125 0 0 35 4 0
## 126 0 0 40 4 1
## 127 0 1719 28 4 0
## 128 0 0 40 4 1
## 129 0 0 40 4 0
## 130 0 0 40 4 0
## 131 0 0 60 4 0
## 132 0 1762 40 4 0
## 133 4386 0 35 4 0
## 134 0 0 50 4 0
## 135 0 0 36 4 0
## 136 0 0 40 5 1
## 137 0 0 60 4 0
## 138 0 0 35 4 0
## 139 0 0 20 4 0
## 140 0 0 40 4 1
## 141 0 0 40 4 0
## 142 0 0 40 4 0
## 143 0 0 30 4 0
## 144 0 1564 40 4 1
## 145 0 0 40 4 0
## 146 0 0 30 4 0
## 147 0 0 24 4 0
## 148 0 0 24 4 0
## 149 0 2179 40 4 0
## 150 0 0 40 4 0
## 151 0 0 38 4 0
## 152 0 0 40 4 0
## 153 0 0 40 2 0
## 154 0 0 35 4 0
## 155 0 0 45 4 1
## 156 0 0 46 4 0
## 157 0 0 40 4 0
## 158 0 1816 2 4 0
## 159 0 0 60 4 0
## 160 0 0 50 4 0
## 161 0 0 20 4 0
## 162 0 0 40 4 0
## 163 0 0 40 4 0
## 164 0 0 40 3 0
## 165 0 0 40 4 1
## 166 0 0 40 4 0
## 167 0 0 42 4 0
## 168 0 0 40 4 0
## 169 0 0 16 4 0
## 170 0 0 20 4 0
## 171 0 1980 40 4 0
## 172 0 0 40 4 0
## 173 0 1977 40 4 1
## 174 0 0 50 4 0
## 175 0 0 40 4 1
## 176 0 0 25 4 0
## 177 0 0 40 4 0
## 178 0 0 40 4 0
## 179 0 0 12 4 0
## 180 0 0 65 4 0
## 181 0 0 38 4 1
## 182 0 0 40 4 0
## 183 0 0 45 4 0
## 184 0 0 40 4 1
## 185 0 0 42 4 1
## 186 0 0 40 4 0
## 187 0 0 40 4 0
## 188 0 0 50 4 0
## 189 0 0 50 4 0
## 190 0 0 1 4 1
## 191 0 0 28 4 0
## 192 0 0 40 4 0
## 193 0 0 40 4 0
## 194 0 0 45 4 0
## 195 0 0 24 4 0
## 196 0 0 40 4 0
## 197 0 0 40 4 0
## 198 14084 0 55 4 1
## 199 7298 0 40 4 1
## 200 0 0 40 4 0
## 201 0 1876 40 4 0
## 202 0 0 40 3 0
## 203 0 0 50 4 1
## 204 0 1340 40 4 0
## 205 0 0 35 4 0
## 206 0 0 40 4 0
## 207 7298 0 36 4 1
## 208 0 0 60 4 0
## 209 15024 0 35 4 1
## 210 0 0 12 4 0
## 211 0 0 40 4 0
## 212 0 0 40 4 1
## 213 0 0 40 4 0
## 214 0 0 40 4 0
## 215 0 0 40 4 1
## 216 0 0 60 4 1
## 217 0 0 40 4 0
## 218 0 0 50 3 0
## 219 0 0 55 4 0
## 220 0 0 40 4 0
## 221 0 0 40 4 0
## 222 0 0 40 4 0
## 223 0 2206 40 4 0
## 224 0 0 20 4 0
## 225 0 0 35 4 0
## 226 0 0 40 4 0
## 227 0 0 10 4 0
## 228 1409 0 50 4 0
## 229 0 0 20 6 0
## 230 0 0 40 4 0
## 231 3674 0 40 4 0
## 232 0 0 38 4 0
## 233 0 0 42 4 0
## 234 0 0 40 4 0
## 235 0 0 55 4 1
## 236 0 0 40 4 0
## 237 0 0 38 4 0
## 238 0 0 50 5 1
## 239 0 0 70 4 1
## 240 0 0 40 4 0
## 241 0 0 40 4 1
## 242 0 0 20 4 0
## 243 0 0 50 4 0
## 244 0 0 20 4 0
## 245 0 1741 40 4 0
## 246 0 0 35 1 0
## 247 0 0 45 4 1
## 248 0 0 34 4 0
## 249 0 1977 60 4 1
## 250 0 0 40 1 0
## 251 0 0 40 4 1
## 252 0 0 40 4 0
## 253 0 0 30 4 0
## 254 0 0 40 4 0
## 255 0 0 20 4 0
## 256 0 0 40 2 1
## 257 0 0 40 4 0
## 258 0 0 30 4 0
## 259 0 0 45 4 0
## 260 0 0 40 4 0
## 261 0 0 40 4 0
## 262 0 0 60 4 0
## 263 0 0 12 4 0
## 264 4064 0 40 4 0
## 265 0 0 40 4 0
## 266 0 0 75 2 1
## 267 0 0 24 4 0
## 268 0 0 40 4 1
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## 270 0 0 40 4 1
## 271 0 0 40 4 1
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## 274 0 0 35 4 0
## 275 0 0 40 4 0
## 276 0 0 40 4 0
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## 278 0 0 40 4 0
## 279 0 0 40 4 0
## 280 7298 0 48 4 1
## 281 0 0 15 3 0
## 282 5178 0 40 4 1
## 283 0 0 45 4 0
## 284 0 0 42 4 0
## 285 0 0 40 4 0
## 286 15024 0 40 4 1
## 287 0 0 60 4 1
## 288 0 0 45 4 0
## 289 0 0 40 4 0
## 290 0 0 40 6 0
## 291 0 0 45 4 0
## 292 0 0 40 4 0
## 293 0 0 56 4 0
## 294 0 0 45 4 0
## 295 0 0 40 4 0
## 296 0 0 16 4 0
## 297 0 0 60 4 0
## 298 3464 0 40 1 0
## 299 0 0 40 4 0
## 300 0 0 80 4 0
## 301 7688 0 40 4 1
## 302 0 0 25 4 0
## 303 0 0 45 4 1
## 304 4386 0 40 4 1
## 305 0 0 40 2 0
## 306 0 0 40 4 0
## 307 0 0 45 4 0
## 308 0 0 33 4 0
## 309 0 0 40 4 1
## 310 0 0 54 4 1
## 311 0 0 40 4 0
## 312 7298 0 35 2 1
## 313 0 0 25 4 0
## 314 0 0 40 4 1
## 315 0 0 15 4 0
## 316 0 0 40 4 0
## 317 0 0 40 4 0
## 318 0 0 40 4 0
## 319 0 0 40 4 0
## 320 0 0 24 4 0
## 321 0 0 40 4 0
## 322 0 1485 50 4 0
## 323 0 0 45 4 0
## 324 0 0 40 4 0
## 325 0 0 10 4 0
## 326 0 0 45 4 0
## 327 0 0 15 4 0
## 328 0 1887 50 4 1
## 329 0 0 40 4 0
## 330 2050 0 55 4 0
## 331 0 0 45 4 0
## 332 0 0 40 4 0
## 333 0 0 40 4 0
## 334 0 0 40 4 0
## 335 0 0 40 4 0
## 336 0 0 48 4 0
## 337 0 0 40 4 0
## 338 0 0 60 4 0
## 339 0 0 30 1 0
## 340 0 0 40 4 0
## 341 0 0 40 4 0
## 342 7298 0 40 4 1
## 343 7298 0 40 4 1
## 344 0 0 20 4 0
## 345 0 0 40 4 0
## 346 0 0 42 4 0
## 347 0 0 8 4 0
## 348 0 0 40 4 0
## 349 0 0 25 5 0
## 350 0 0 25 4 0
## 351 0 0 40 4 1
## 352 0 0 40 4 0
## 353 0 0 40 4 1
## 354 0 0 30 4 0
## 355 0 0 40 4 1
## 356 0 0 40 4 1
## 357 0 0 40 4 0
## 358 0 0 40 4 1
## 359 0 0 35 4 0
## 360 0 0 40 3 0
## 361 0 0 24 4 0
## 362 0 0 40 4 1
## 363 0 0 45 4 0
## 364 0 1564 45 4 1
## 365 0 0 55 4 0
## 366 0 0 40 4 0
## 367 0 0 45 4 0
## 368 0 0 15 4 0
## 369 0 0 45 4 1
## 370 0 0 40 4 0
## 371 0 0 15 4 0
## 372 0 0 10 4 0
## 373 0 0 38 4 1
## 374 0 0 40 4 0
## 375 0 0 40 4 0
## 376 0 0 10 4 0
## 377 0 0 40 4 0
## 378 0 0 40 4 0
## 379 0 0 30 4 0
## 380 0 0 50 4 0
## 381 0 0 40 4 0
## 382 0 2339 45 4 0
## 383 0 0 40 4 0
## 384 0 0 40 4 0
## 385 0 0 40 4 0
## 386 0 0 25 4 0
## 387 0 0 40 4 0
## 388 0 2415 6 4 1
## 389 0 0 16 4 0
## 390 0 0 40 4 0
## 391 0 0 40 4 1
## 392 0 0 50 4 0
## 393 0 0 40 4 0
## 394 7688 0 45 1 1
## 395 0 0 35 4 0
## 396 0 0 40 4 0
## 397 0 0 40 4 0
## 398 0 0 35 4 0
## 399 0 0 55 4 1
## 400 0 0 50 4 1
## 401 0 0 40 4 0
## 402 0 0 40 4 0
## 403 0 0 40 4 1
## 404 0 0 64 4 0
## 405 0 2179 40 7 0
## 406 0 1977 65 4 1
## 407 0 0 40 4 0
## 408 0 0 25 4 0
## 409 0 1887 40 4 1
## 410 0 0 50 4 0
## 411 0 0 24 4 0
## 412 0 0 40 4 0
## 413 0 0 40 4 0
## 414 15024 0 60 4 1
## 415 0 0 40 4 0
## 416 0 0 50 4 1
## 417 0 0 40 4 0
## 418 0 0 19 4 0
## 419 0 0 40 4 0
## 420 0 0 40 4 0
## 421 0 0 30 4 0
## 422 2176 0 18 4 0
## 423 0 0 40 4 1
## 424 0 0 40 4 0
## 425 0 0 35 4 0
## 426 0 0 60 4 0
## 427 0 1408 40 4 0
## 428 0 0 72 1 0
## 429 0 0 40 4 1
## 430 0 0 40 4 0
## 431 0 0 24 4 0
## 432 0 0 40 4 0
## 433 2174 0 40 4 0
## 434 0 0 25 4 1
## 435 0 1980 40 2 0
## 436 0 0 50 4 1
## 437 594 0 60 4 0
## 438 0 0 40 4 0
## 439 0 0 40 4 0
## 440 0 0 40 4 0
## 441 0 0 40 4 0
## 442 0 0 28 4 0
## 443 0 0 40 4 0
## 444 0 0 40 4 1
## 445 0 0 40 4 0
## 446 0 0 35 4 0
## 447 0 0 40 4 0
## 448 0 0 40 4 0
## 449 0 0 40 4 0
## 450 0 0 5 4 0
## 451 0 0 40 4 0
## 452 0 0 55 4 1
## 453 0 0 40 4 1
## 454 0 0 40 1 1
## 455 0 0 40 4 1
## 456 0 0 40 4 1
## 457 0 0 40 4 0
## 458 0 0 50 4 0
## 459 0 0 60 4 0
## 460 0 0 40 4 0
## 461 0 0 40 4 0
## 462 0 0 40 4 0
## 463 0 0 50 4 0
## 464 0 0 40 4 0
## 465 0 0 20 4 0
## 466 0 0 60 4 0
## 467 0 0 40 4 0
## 468 0 0 40 4 0
## 469 0 1977 45 4 1
## 470 0 0 16 4 1
## 471 0 0 40 4 1
## 472 0 0 40 4 0
## 473 0 0 40 4 0
## 474 0 0 60 4 0
## 475 0 0 24 4 0
## 476 0 0 40 4 0
## 477 0 0 6 4 0
## 478 0 0 30 4 0
## 479 0 0 40 4 0
## 480 0 0 35 4 0
## 481 0 0 45 3 0
## 482 0 0 20 4 0
## 483 0 0 20 4 0
## 484 0 0 40 4 0
## 485 594 0 40 4 0
## 486 0 0 20 4 0
## 487 0 0 35 4 0
## 488 0 0 45 4 0
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## [ reached 'max' / getOption("max.print") -- omitted 26680 rows ]Verify all missing values are adjusted.
adult <- adult %>%
mutate(invested = ifelse(capital_gain > 0 | capital_loss > 0, 1, 0))
adult$invested <- factor(adult$invested,
levels = c("0", "1"),
labels = c("Not_Invested", "Invested")
)
levels(adult$invested)## [1] "Not_Invested" "Invested"# Get all unique values in'invested'
unique_values <- unique(adult$invested)
unique_values## [1] Invested Not_Invested
## Levels: Not_Invested Invested#Checking data types
str(adult)## 'data.frame': 32562 obs. of 18 variables:
## $ age : num 39 50 38 53 28 37 49 52 31 42 ...
## $ workclass : Factor w/ 8 levels "unemployed","FedGov",..: 8 7 5 5 5 5 5 7 5 5 ...
## $ similar_pop_count: num 77516 83311 215646 234721 338409 ...
## $ education_level : Factor w/ 8 levels "assoc","highsch",..: 8 8 2 2 8 3 2 2 3 8 ...
## $ years_education : num 13 13 9 7 13 14 5 9 14 13 ...
## $ marital_status : Factor w/ 4 levels "divorce","married",..: 3 2 1 2 2 2 2 2 3 2 ...
## $ occupation : Factor w/ 6 levels "agriculture",..: 2 3 4 4 3 3 4 3 3 3 ...
## $ household_role : Factor w/ 6 levels "husband","wife",..: 3 1 3 1 2 2 3 1 3 1 ...
## $ race : Factor w/ 5 levels "other","white",..: 2 2 2 3 3 2 3 2 2 2 ...
## $ gender : Factor w/ 2 levels "female","male": 2 2 2 2 1 1 1 2 1 2 ...
## $ capital_gain : num 2174 0 0 0 0 ...
## $ capital_loss : num 0 0 0 0 0 0 0 0 0 0 ...
## $ hours_per_week : num 40 13 40 40 40 40 16 45 50 40 ...
## $ native_region : Factor w/ 7 levels "CentrAmerica",..: 4 4 4 4 1 4 1 4 4 4 ...
## $ income : Factor w/ 2 levels "<=50K",">50K": 1 1 1 1 1 1 1 2 2 2 ...
## $ hours_worked_span: Factor w/ 6 levels "1-15","16-30",..: 3 1 3 3 3 3 2 3 4 3 ...
## $ age_span : Ord.factor w/ 6 levels "1-20"<"21-35"<..: 3 4 3 4 2 3 3 4 2 3 ...
## $ invested : Factor w/ 2 levels "Not_Invested",..: 2 1 1 1 1 1 1 1 2 2 ...cat("\n------------------Count NA----------------------\n\n")##
## ------------------Count NA----------------------sort(colSums(is.na(adult)), decreasing = TRUE)## age workclass similar_pop_count education_level
## 0 0 0 0
## years_education marital_status occupation household_role
## 0 0 0 0
## race gender capital_gain capital_loss
## 0 0 0 0
## hours_per_week native_region income hours_worked_span
## 0 0 0 0
## age_span invested
## 0 0vis_miss(adult)
dim(adult)## [1] 32562 18EDA of the variables
Sources: https://oakleyj.github.io/MAS61004/eda-for-logistic-regression.html
# histogram of age by gender group
ggplot(adult) + aes(x=age, group=income, fill=income) +
geom_histogram(binwidth=1, color='black') +
labs(title = "Income by Age", x = "Age")
ggplot(adult) + aes(x=years_education, group=income, fill=income) +
geom_histogram(binwidth=1, color='black') +
labs(title = "Income by Years of Education", x = "Years")
ggplot(adult) + aes(x=hours_per_week, group=income, fill=income) +
geom_histogram(binwidth=1, color='black') +
labs(title = "Income by Hours Per Week", x = "Hours")
# Income by gain
qplot(capital_gain, data = adult, fill = income)+
ggtitle('Earnings with Capital Gains')## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Income by loss
qplot(capital_loss, data = adult, fill = income)+
ggtitle('Earnings with Capital Loss')## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(adult, aes(x = invested, y = capital_gain, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Investment Gain")
ggplot(adult, aes(x = invested, y = capital_loss, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Investment Loss")
# Income by hours with percentage
ggplot(adult, aes(x = hours_per_week, fill = income)) +
geom_bar(position = "fill") +
# facet_grid(. ~ race) +
scale_y_continuous(labels = scales::percent_format()) +
labs(y = "Percentage")+
ggtitle("Earnings by Hours") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
# Log-transform the 'capital_gain' column
adult_dum$capital_gain_log <- log(adult_dum$capital_gain + 1) # Adding 1 to avoid log(0)
# Log-transform the 'capital_gain' column
adult_dum$capital_loss_log <- log(adult_dum$capital_loss + 1) # Adding 1 to avoid log(0)library(tidyverse)
library(broom)
# Check the continuous variables for linearity
ggplot(adult_dum,(aes(x=age,y=income)))+geom_point()+geom_smooth(method="loess",span=.1) +
labs(title = "Income by Age", x = "Age", y = "Income")## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult_dum,(aes(x=years_education,y=income)))+geom_point()+geom_smooth(method="loess",span=.6)+
labs(title = "Income by Years of Education", x = "Years", y = "Income")## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult_dum,(aes(x=hours_per_week,y=income)))+geom_point()+geom_smooth(method="loess",span=.6)+
labs(title = "Income by Hours Per Week", x = "Hours", y = "Income")## `geom_smooth()` using formula = 'y ~ x'
# Non logged capital Gains
ggplot(adult, aes(x=capital_gain, y=adult_dum$income)) +
geom_point() +
geom_smooth(method="loess", size=1, span=1) +
ylim(-.2, 2.5) +
labs(title = "Income by Capital Gain", x = "Capital Gain", y = "Income")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_smooth()` using formula = 'y ~ x'
# Non-logged capital loss
ggplot(adult, aes(x=capital_loss, y=adult_dum$income)) +
geom_point() +
geom_smooth(method="loess", size=1, span=1) +
ylim(-.2, 2.5) +
labs(title = "Income by Capital Loss", x = "Capital Loss", y = "Income")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 2 rows containing missing values (`geom_smooth()`).
# Logged gain and loss
ggplot(adult_dum,(aes(x=capital_gain_log, y=income)))+geom_point()+geom_smooth(method="loess",span=1)+
labs(title = "Income by Log Capital Gain", x = "Capital Gain", y = "Income")## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult_dum,(aes(x=capital_loss_log, y=income)))+geom_point()+geom_smooth(method="loess",span=1)+
labs(title = "Income by Log Capital Loss", x = "Capital Loss", y = "Income")## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult, aes(x = gender, y = age, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Age and Gender")
ggplot(adult, aes(x = gender, y = years_education, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Gender and Education")
ggplot(adult, aes(x = gender, y = hours_per_week, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Hours Worked and Gender")
ggplot(adult, aes(x = income, y = hours_per_week, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Hours Worked")
# Income by gender and hourswith percentage
ggplot(adult, aes(x = age, fill = income)) +
geom_bar(width = 1, position = "fill") +
facet_grid(. ~ gender) +
scale_y_continuous(labels = scales::percent_format()) +
labs(y = "Percentage")+
ggtitle("Earnings by Age Type and Gender") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))## Warning: `position_stack()` requires non-overlapping x intervals
# Income by gender and hourswith percentage
ggplot(adult, aes(x = hours_per_week, fill = income)) +
geom_bar(width = 1, position = "fill") +
facet_grid(. ~ gender) +
scale_y_continuous(labels = scales::percent_format()) +
labs(y = "Percentage")+
ggtitle("Earnings by Hours Worked Type and Gender") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))## Warning: `position_stack()` requires non-overlapping x intervals
# Income by gender and education with percentage
ggplot(adult, aes(x = years_education, fill = income)) +
geom_bar(width = 0.8, position = "fill") +
facet_grid(. ~ gender) +
scale_y_continuous(labels = scales::percent_format()) +
labs(y = "Percentage")+
ggtitle("Earnings by Years Education and Gender") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))## Warning: `position_stack()` requires non-overlapping x intervals
# Check the continuous variables for linearity against the logodds of Y
levels(adult$gender)## [1] "female" "male"# Adjusted code for Age (AG) by weight (WT), with y = Status.Num
ggplot(adult_dum, aes(x=age, y=income, colour=as.factor(gender_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=.75) +
ylim(-.2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by Age with Gender", x = "Age", y = "Income") +
scale_colour_manual(name = "Gender",
values = c("1" = "orange",
"2" = "green"),
labels = c("Female", "Male")) ## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult_dum, aes(x=hours_per_week, y=income, colour=as.factor(gender_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=.75) +
ylim(-.2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by hours Worked with Gender", x = "Hours", y = "Income") +
scale_colour_manual(name = "Gender",
values = c("1" = "orange",
"2" = "green"),
labels = c("Female", "Male")) ## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult_dum, aes(x=years_education, y=income, colour=as.factor(gender_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=.75) +
ylim(-.2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by Years of Education with Gender", x = "Years", y = "Income")+
scale_colour_manual(name = "Gender",
values = c("1" = "orange",
"2" = "green"),
labels = c("Female", "Male")) ## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult, aes(x = race, y = age, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Age and Race")
ggplot(adult, aes(x = race, y = years_education, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Education and Race")
ggplot(adult, aes(x = race, y = hours_per_week, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Hours Worked and Race")
# Income by occupation
qplot(occupation, data = adult, fill = income) + facet_grid (. ~ race)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
# Income by race with percentage
ggplot(adult, aes(x = occupation, fill = income)) +
geom_bar(position = "fill") +
facet_grid(. ~ race) +
scale_y_continuous(labels = scales::percent_format()) +
labs(y = "Percentage")+
ggtitle("Earnings by Occupation and Race") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
# Income by race
qplot(education_level, data = adult, fill = income) +
facet_grid(. ~ race) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
# Income by race with percentage
ggplot(adult, aes(x = education_level, fill = income)) +
geom_bar(position = "fill") +
facet_grid(. ~ race) +
scale_y_continuous(labels = scales::percent_format()) +
labs(y = "Percentage")+
ggtitle("Earnings by Education Type and Race") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
# Income by race with percentage
ggplot(adult, aes(x = marital_status, fill = income)) +
geom_bar(position = "fill") +
facet_grid(. ~ race) +
scale_y_continuous(labels = scales::percent_format()) +
labs(y = "Percentage")+
ggtitle("Earnings by Marital Status and Race (Percentage") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
# Hourse worked
ggplot(adult, aes(x = occupation, y = hours_per_week, colour = income, fill = income)) +
geom_boxplot(color='black') +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
facet_grid(. ~ race)+
ggtitle("Earnings by Race, Occupation, and Hours Worked")
# Check the continuous variables for linearity against the logodds of Y
# Adjusted code for Age (AG) by weight (WT), with y = Status.Num
ggplot(adult_dum, aes(x=age, y=income, colour=as.factor(race_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=.75) +
ylim(-.2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by Age with Race", x = "Age", y = "Income") +
scale_colour_manual(name = "Race",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3",
"5" = "red"),
labels = c("Other", "White", "Black", "Native", "Asian/Pacific Islander")) ## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult_dum, aes(x=hours_per_week, y=income, colour=as.factor(race_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=.75) +
ylim(-.2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by hours Worked with Race", x = "Hours", y = "Income") +
scale_colour_manual(name = "Race",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3",
"5" = "red"),
labels = c("Other", "White", "Black", "Native", "Asian/Pacific Islander")) ## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult_dum, aes(x=years_education, y=income, colour=as.factor(race_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=.75) +
ylim(-.2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by Years of Education with Race", x = "Years", y = "Income")+
scale_colour_manual(name = "Race",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3",
"5" = "red"),
labels = c("Other", "White", "Black", "Native", "Asian/Pacific Islander")) ## `geom_smooth()` using formula = 'y ~ x'
# Income by gender
qplot(race, data = adult, fill = income) +
facet_grid(. ~ marital_status) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
ggtitle('Earnings with Race and Marital Status')
# Income by gender
qplot(education_level, data = adult, fill = income) +
facet_grid(. ~ marital_status) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
ggtitle('Earnings with Education and Marital Status')
# Income by gender
qplot(occupation, data = adult, fill = income) +
facet_grid(. ~ marital_status) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
ggtitle('Earnings with Occupation and Marital Status')
# Check the continuous variables for linearity against the logodds of Y
levels(adult$marital_status)## [1] "divorce" "married" "notmarried" "widowed"# Adjusted code for Age (AG) by weight (WT), with y = Status.Num
ggplot(adult_dum, aes(x=age, y=income, colour=as.factor(marital_status_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=.75) +
ylim(-.2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by Age with Marital Status", x = "Age", y = "Income") +
scale_colour_manual(name = "Marital Status",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3"),
labels = c("divorced", "married", "notmarried", "widowed")) ## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult_dum, aes(x=hours_per_week, y=income, colour=as.factor(marital_status_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=.75) +
ylim(-.2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by hours Worked with Marital Status", x = "Hours", y = "Income") +
scale_colour_manual(name = "Marital Status",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3"),
labels = c("divorced", "married", "notmarried", "widowed")) ## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult_dum, aes(x=years_education, y=income, colour=as.factor(marital_status_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=.75) +
ylim(-.2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by Years of Education with Marital Status", x = "Years", y = "Income")+
scale_colour_manual(name = "Marital Status",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3"),
labels = c("divorced", "married", "notmarried", "widowed")) ## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult, aes(x = hours_worked_span, y = age, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Hours Worked")
ggplot(adult, aes(x = income, y = age, color=income, fill=income)) +
geom_boxplot(color='black') +
facet_grid(. ~ native_region) +
ggtitle("Earnings by Age and Origin")
ggplot(adult) + aes(x=hours_per_week, group=income, fill=income) +
geom_histogram()+
facet_grid(. ~ occupation) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Income by race with percentage
ggplot(adult, aes(x = hours_worked_span, fill = income)) +
geom_bar(position = "fill") +
facet_grid(. ~ occupation) +
scale_y_continuous(labels = scales::percent_format()) +
labs(y = "Percentage")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
# Income by race with percentage
ggplot(adult, aes(x = native_region, fill = income)) +
geom_bar(position = "fill") +
facet_grid(. ~ occupation) +
scale_y_continuous(labels = scales::percent_format()) +
labs(y = "Percentage")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
ggplot(adult, aes(x = marital_status, y = hours_per_week, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Marital Status and Education")
# Hourse worked
ggplot(adult, aes(x = gender, y = hours_per_week, colour = income, fill = income)) +
geom_boxplot(color='black') +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
facet_grid(. ~ race)+
ggtitle("Earnings by Race, Gender, and Hours Worked")
ggplot(adult, aes(x = gender, y = years_education, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Gender and Education")
ggplot(adult, aes(x = marital_status, y = hours_per_week, colour = income, fill = income)) +
geom_boxplot(color='black') +
ggtitle("Earnings by Marital Status and Hours")
# Income by gender
qplot(race, data = adult, fill = income) +
facet_grid(. ~ marital_status) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
ggtitle('Earnings with Race and Marital Status')
# Income by occupation
qplot(income, data = adult, fill = income) + facet_grid (. ~ occupation)+
ggtitle('Earnings with Occupation')
# Income by education
qplot(income, data = adult, fill = income) + facet_grid (. ~ education_level)+
ggtitle('Earnings with Education')
# Income by race
qplot(education_level, data = adult, fill = income) +
facet_grid(. ~ race) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))+
ggtitle('Earnings with Race and Education')
library(treemap)
adult3 <- adult
# Log-transform the 'capital_gain' column
adult3$capital_gain_log <- log(adult3$capital_gain + 1) # Adding 1 to avoid log(0)
# Log-transform the 'capital_gain' column
adult3$capital_loss_log <- log(adult3$capital_loss + 1) # Adding 1 to avoid log(0)
## Native REGION and Income Status
adult3$income.num<-ifelse(adult3$income==">50K", 1, 0)
# Calculate proportions
positive_proportions2 <- table(adult3$native_region, adult3$income.num)[, "1"] / rowSums(table(adult3$native_region, adult3$income.num))
# Create a data frame
treemap_data2 <- data.frame(native_region = names(positive_proportions2), positive_proportion = positive_proportions2)
# Sort by positive_proportion
treemap_data2 <- treemap_data2[order(treemap_data2$positive_proportion, decreasing = TRUE), ]
# Plot the treemap
treemap(treemap_data2,
index = "native_region",
vSize = "positive_proportion",
title = "Proportion of Positive Income Status by Native Region of Origin")
library(treemap)
# Heat map
income_status_table <- table(adult$native_region, adult_dum$income)
positive_counts <- income_status_table[, "1"]
total_counts <- rowSums(income_status_table)
positive_proportions<-positive_counts/total_counts
heatmap_data <- data.frame(native_country = names(positive_proportions), positive_proportion = positive_proportions)
heatmap_data$native_country <- factor(heatmap_data$native_country, levels = heatmap_data$native_country[order(heatmap_data$positive_proportion, decreasing = FALSE)])
ggplot(heatmap_data, aes(x = "", y = native_country, fill = positive_proportion)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "blue") +
theme_minimal() +
labs(title = "Proportion of Positive Income Status by native_country of Origin", x = NULL, y = NULL) +
scale_colour_manual(name = "Region",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3",
"5" = "red",
"6" = "purple",
"7" = "brown"),
labels = c("EAsia", "SAsia", "North America", "Central America", "South America", "Europe", "US Territory")) 
# Check the continuous variables for linearity against the logodds of Y
# Adjusted code for Age (AG) by weight (WT), with y = Status.Num
ggplot(adult_dum, aes(x=capital_gain_log, y=income, colour=as.factor(native_region_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=2) +
ylim(-2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by Capital Gain with Region", x = "Gain", y = "Income") +
scale_colour_manual(name = "Region",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3",
"5" = "red",
"6" = "purple",
"7" = "brown"),
labels = c("EAsia", "SAsia", "North America", "Central America", "South America", "Europe", "US Territory")) ## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 22 rows containing missing values (`geom_smooth()`).
# Adjusted code for Age (AG) by weight (WT), with y = Status.Num
ggplot(adult_dum, aes(x=capital_loss_log, y=income, colour=as.factor(native_region_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=2) +
ylim(-2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by Capital Loss with Region", x = "Loss", y = "Income") +
scale_colour_manual(name = "Region",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3",
"5" = "red",
"6" = "purple",
"7" = "brown"),
labels = c("EAsia", "SAsia", "North America", "Central America", "South America", "Europe", "US Territory")) ## `geom_smooth()` using formula = 'y ~ x'
# Adjusted code for Age (AG) by weight (WT), with y = Status.Num
ggplot(adult_dum, aes(x=years_education, y=income, colour=as.factor(native_region_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=2) +
ylim(-2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by Years of Education with Region", x = "Years", y = "Income") +
scale_colour_manual(name = "Region",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3",
"5" = "red",
"6" = "purple",
"7" = "brown"),
labels = c("EAsia", "SAsia", "North America", "Central America", "South America", "Europe", "US Territory")) ## `geom_smooth()` using formula = 'y ~ x'
# Adjusted code for Age (AG) by weight (WT), with y = Status.Num
ggplot(adult_dum, aes(x=occupation_num, y=income, colour=as.factor(native_region_num))) +
geom_point() +
geom_smooth(method="loess", size=1, span=2) +
ylim(-2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by Occupation with Region", x = "Occupation", y = "Income") +
scale_colour_manual(name = "Region",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3",
"5" = "red",
"6" = "purple",
"7" = "brown"),
labels = c("EAsia", "SAsia", "North America", "Central America", "South America", "Europe", "US Territory")) ## `geom_smooth()` using formula = 'y ~ x'
ggplot(adult_dum,(aes(x=capital_gain_log,y=income, colour=as.factor(race_num))))+geom_point()+geom_smooth(method="loess",span=2) +
ylim(-2, 1.2) +
# facet_wrap(~factor(Pclass)) +
labs(title = "Income by capital gain with Race", x = "Gain", y = "Income") +
scale_colour_manual(name = "Race",
values = c("1" = "orange",
"2" = "green",
"3" = "#619CFF",
"4" = "#F564E3",
"5" = "red"),
labels = c("Other", "White", "Black", "Native", "Asian/Pacific Islander")) ## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 18 rows containing missing values (`geom_smooth()`).
adult2 <- adult
# Log-transform the 'capital_gain' column
adult2$capital_gain_log <- log(adult2$capital_gain + 1) # Adding 1 to avoid log(0)
# Log-transform the 'capital_gain' column
adult2$capital_loss_log <- log(adult2$capital_loss + 1) # Adding 1 to avoid log(0)
# Drop the specified columns
adult2 <- subset(adult2, select = -c(age_span, hours_worked_span, similar_pop_count, capital_gain, capital_loss, invested))#Check Logistic Assumptions: Sources: https://www.statology.org/assumptions-of-logistic-regression/ * https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/assumptions-of-logistic-regression/ * https://r4ds.github.io/bookclub-islr/addendum---logistic-regression-assumptions.html * https://sscc.wisc.edu/sscc/pubs/RegDiag-R/logistic-regression.html#log_ind * http://www.sthda.com/english/articles/36-classification-methods-essentials/148-logistic-regression-assumptions-and-diagnostics-in-r/#google_vignette * https://towardsdatascience.com/assumptions-of-logistic-regression-clearly-explained-44d85a22b290 * Assumption 1: Is the response variable binary?
# Assure it is a factor
adult2$income <- factor(adult2$income, levels = c("<=50K", ">50K"))
levels(adult2$income)## [1] "<=50K" ">50K"# Visual inspection (example with 'age' as a predictor)
ggplot(adult2, aes(x = age, fill = income)) + geom_histogram(position = "dodge")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Assumption 2: Independence of Observations
- The Durbin-Watson statistic (DW) is close to 2 (DW = 1.9966), which indicates no autocorrelation, and the high p-value (p-value = 0.3786) means that you fail to reject the null hypothesis of no autocorrelation
# Fit the logistic regression model
model <- glm(income ~ ., data = adult2, family = binomial)
# Perform the Durbin-Watson test
dw_result <- dwtest(model)
dw_result##
## Durbin-Watson test
##
## data: model
## DW = 1.9975, p-value = 0.4118
## alternative hypothesis: true autocorrelation is greater than 0Assumption 3: No Multicollinearity among predictors.
- All Variance Inflation Factors (VIFs) are below 5 and visually verified as not being highly correlated.
- Note: a VIF value below 5 suggests that there is no significant multicollinearity among the predictors, and values below 10 are often considered acceptable.
adult_dum <- adult2
# one-hot-encode income
adult_dum$income <- ifelse(adult_dum$income == "<=50K", 0, 1)
# Loop through columns
for (col in names(adult_dum)) {
if (is.factor(adult_dum[[col]])) { # Check if column is a factor
levels <- levels(adult_dum[[col]]) # Get levels of the factor
adult_dum[[col]] <- as.numeric(factor(adult_dum[[col]], levels = levels)) # Convert factor to numeric based on levels
names(adult_dum)[names(adult_dum) == col] <- paste0(col, "_num") # Rename the column
}
}
adult_dum## age workclass_num education_level_num years_education marital_status_num
## 1 39 8 8 13 3
## 2 50 7 8 13 2
## 3 38 5 2 9 1
## 4 53 5 2 7 2
## 5 28 5 8 13 2
## 6 37 5 3 14 2
## 7 49 5 2 5 2
## 8 52 7 2 9 2
## 9 31 5 3 14 3
## 10 42 5 8 13 2
## 11 37 5 1 10 2
## 12 30 8 8 13 2
## 13 23 5 8 13 3
## 14 32 5 1 12 3
## 15 40 5 1 11 2
## 16 34 5 7 4 2
## 17 25 7 2 9 3
## 18 32 5 2 9 3
## 19 38 5 2 7 2
## 20 43 7 3 14 1
## 21 40 5 5 16 2
## 22 54 5 2 9 1
## 23 35 2 2 5 2
## 24 43 5 2 7 2
## 25 59 5 2 9 1
## 26 56 3 8 13 2
## 27 19 5 2 9 3
## 28 54 1 1 10 2
## 29 39 5 2 9 1
## 30 49 5 2 9 2
## 31 23 3 1 12 3
## 32 20 5 1 10 3
## 33 45 5 8 13 1
## 34 30 2 1 10 2
## 35 22 8 1 10 2
## 36 48 5 2 7 3
## 37 21 5 1 10 3
## 38 19 5 2 9 2
## 39 31 5 1 10 2
## 40 48 7 1 12 2
## 41 31 5 2 5 2
## 42 53 7 8 13 2
## 43 24 5 8 13 2
## 44 49 5 2 9 1
## 45 25 5 2 9 3
## 46 57 2 8 13 2
## 47 53 5 2 9 2
## 48 44 5 3 14 1
## 49 41 8 1 11 2
## 50 29 5 1 11 3
## 51 25 5 1 10 2
## 52 18 5 2 9 3
## 53 47 5 8 15 2
## 54 50 2 8 13 1
## 55 47 6 2 9 1
## 56 43 5 1 10 2
## 57 46 5 6 3 2
## 58 35 5 1 11 2
## 59 41 5 2 9 2
## 60 30 5 2 9 2
## 61 30 5 8 13 2
## 62 32 1 7 4 2
## 63 48 5 2 9 2
## 64 42 5 5 16 2
## 65 29 5 1 10 1
## 66 36 5 2 9 2
## 67 28 5 1 10 1
## 68 53 5 2 9 2
## 69 49 6 1 10 2
## 70 25 1 1 10 3
## 71 19 5 1 10 3
## 72 31 5 8 13 1
## 73 29 7 8 13 2
## 74 23 5 1 10 3
## 75 79 5 1 10 2
## 76 27 5 2 9 3
## 77 40 5 1 12 2
## 78 67 1 2 6 2
## 79 18 5 2 7 3
## 80 31 3 7 4 2
## 81 18 5 2 9 3
## 82 52 5 8 13 2
## 83 46 5 2 9 2
## 84 59 5 2 9 2
## 85 44 5 2 9 1
## 86 53 5 2 9 1
## 87 49 3 2 9 2
## 88 33 5 3 14 2
## 89 30 5 2 5 3
## 90 43 2 5 16 3
## 91 57 5 1 11 2
## 92 37 5 1 10 1
## 93 28 5 1 10 1
## 94 30 5 2 9 2
## 95 34 3 8 13 2
## 96 29 3 1 10 3
## 97 48 7 5 16 2
## 98 37 5 1 10 2
## 99 48 5 1 12 1
## 100 32 2 2 9 3
## 101 76 5 3 14 2
## 102 44 5 8 13 2
## 103 47 7 3 14 3
## 104 20 5 1 10 3
## 105 29 5 2 9 3
## 106 32 6 2 9 2
## 107 17 1 2 6 3
## 108 30 5 2 7 3
## 109 31 3 2 9 3
## 110 42 5 2 9 2
## 111 24 5 8 13 3
## 112 38 5 8 15 2
## 113 56 7 2 9 2
## 114 28 5 1 10 2
## 115 36 5 2 9 3
## 116 53 5 2 5 2
## 117 56 6 1 10 2
## 118 49 3 1 11 2
## 119 55 5 1 10 2
## 120 22 5 2 9 3
## 121 21 5 1 10 3
## 122 40 5 8 13 2
## 123 30 5 8 13 3
## 124 29 8 8 13 2
## 125 19 5 1 10 3
## 126 47 5 8 13 2
## 127 20 5 1 10 3
## 128 31 5 1 12 2
## 129 35 1 2 9 2
## 130 39 5 1 10 1
## 131 28 5 1 12 3
## 132 24 5 2 9 3
## 133 38 7 2 9 2
## 134 37 5 8 13 2
## 135 46 5 1 12 1
## 136 38 2 3 14 2
## 137 43 7 2 9 2
## 138 27 5 1 11 2
## 139 20 5 1 10 3
## 140 49 5 1 10 2
## 141 61 6 2 9 2
## 142 27 5 1 10 3
## 143 19 5 1 10 3
## 144 45 5 1 11 3
## 145 70 5 1 10 3
## 146 31 5 2 9 3
## 147 22 5 1 10 2
## 148 36 5 2 9 4
## 149 64 5 2 7 2
## 150 43 1 1 10 1
## 151 47 3 1 10 1
## 152 34 5 2 9 3
## 153 33 5 8 13 3
## 154 21 5 2 9 3
## 155 52 1 2 9 1
## 156 48 5 2 9 2
## 157 23 5 8 13 3
## 158 71 7 1 10 1
## 159 29 5 2 9 1
## 160 42 5 8 13 1
## 161 68 1 6 2 1
## 162 25 5 2 9 3
## 163 44 6 3 14 1
## 164 28 5 2 9 3
## 165 45 7 3 14 2
## 166 36 5 1 10 1
## 167 39 2 1 12 3
## 168 46 8 3 14 4
## 169 18 5 2 7 3
## 170 66 3 1 11 4
## 171 27 5 2 9 3
## 172 28 8 2 9 2
## 173 51 5 1 10 2
## 174 27 5 8 13 3
## 175 28 8 8 13 2
## 176 27 5 2 9 2
## 177 21 5 2 9 3
## 178 34 5 2 9 3
## 179 18 5 2 9 3
## 180 33 5 8 13 2
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## 7070 5 3 2 2 40
## 7071 1 3 2 2 40
## 7072 2 1 2 2 45
## 7073 5 1 5 2 40
## 7074 5 1 2 2 44
## 7075 3 1 2 2 40
## 7076 6 4 3 1 30
## 7077 2 3 2 1 40
## 7078 5 6 2 2 40
## 7079 3 4 4 1 40
## 7080 5 1 2 2 40
## 7081 3 6 1 1 20
## 7082 4 3 2 1 25
## 7083 3 3 2 1 65
## 7084 3 2 2 1 40
## 7085 3 1 2 2 40
## 7086 3 1 2 2 50
## 7087 4 6 2 2 20
## 7088 3 3 2 1 40
## 7089 5 1 2 2 70
## 7090 4 1 2 2 40
## 7091 3 1 2 2 65
## 7092 4 4 3 1 40
## 7093 2 3 2 1 39
## 7094 3 2 2 1 40
## 7095 2 4 3 1 40
## 7096 3 1 2 2 30
## 7097 5 3 2 2 40
## 7098 1 1 2 2 60
## 7099 3 3 2 1 20
## 7100 3 1 2 2 50
## 7101 6 1 2 2 80
## 7102 5 3 2 1 56
## 7103 6 6 2 1 40
## 7104 3 1 2 2 40
## 7105 3 1 2 2 40
## 7106 3 4 2 1 50
## 7107 6 4 2 1 28
## 7108 3 3 2 1 40
## 7109 2 3 2 1 40
## 7110 3 1 2 1 40
## 7111 3 3 2 2 50
## 7112 1 3 2 2 40
## 7113 3 2 2 1 60
## 7114 3 3 2 2 55
## 7115 3 1 2 2 40
## 7116 3 6 2 1 25
## 7117 1 6 2 2 20
## 7118 3 1 2 2 45
## 7119 5 3 2 2 50
## 7120 3 3 2 1 6
## 7121 3 1 2 2 35
## 7122 5 3 2 1 40
## 7123 5 1 2 2 40
## 7124 3 1 2 2 40
## 7125 4 1 2 2 40
## 7126 3 5 2 1 10
## 7127 2 3 2 2 25
## 7128 3 3 2 2 48
## 7129 5 3 2 2 15
## 7130 3 3 2 2 44
## 7131 3 1 2 2 30
## 7132 3 2 2 1 45
## 7133 2 6 2 1 20
## 7134 1 4 2 2 40
## 7135 3 1 2 2 40
## 7136 5 6 2 2 32
## 7137 6 1 2 2 13
## 7138 4 5 3 1 40
## 7139 4 1 2 2 40
## 7140 5 1 2 2 40
## 7141 5 1 2 2 50
## 7142 2 3 2 1 40
## native_region_num income capital_gain_log capital_loss_log
## 1 4 0 7.684784 0.000000
## 2 4 0 0.000000 0.000000
## 3 4 0 0.000000 0.000000
## 4 4 0 0.000000 0.000000
## 5 1 0 0.000000 0.000000
## 6 4 0 0.000000 0.000000
## 7 1 0 0.000000 0.000000
## 8 4 1 0.000000 0.000000
## 9 4 1 9.552866 0.000000
## 10 4 1 8.552367 0.000000
## 11 4 1 0.000000 0.000000
## 12 5 1 0.000000 0.000000
## 13 4 0 0.000000 0.000000
## 14 4 0 0.000000 0.000000
## 15 1 1 0.000000 0.000000
## 16 4 0 0.000000 0.000000
## 17 4 0 0.000000 0.000000
## 18 4 0 0.000000 0.000000
## 19 4 0 0.000000 0.000000
## 20 4 1 0.000000 0.000000
## 21 4 1 0.000000 0.000000
## 22 4 0 0.000000 0.000000
## 23 4 0 0.000000 0.000000
## 24 4 0 0.000000 7.622175
## 25 4 0 0.000000 0.000000
## 26 4 1 0.000000 0.000000
## 27 4 0 0.000000 0.000000
## 28 6 1 0.000000 0.000000
## 29 4 0 0.000000 0.000000
## 30 4 0 0.000000 0.000000
## 31 4 0 0.000000 0.000000
## 32 4 0 0.000000 0.000000
## 33 4 0 0.000000 7.250636
## 34 4 0 0.000000 0.000000
## 35 4 0 0.000000 0.000000
## 36 7 0 0.000000 0.000000
## 37 4 0 0.000000 0.000000
## 38 4 0 0.000000 0.000000
## 39 1 1 0.000000 0.000000
## 40 4 0 0.000000 0.000000
## 41 4 0 0.000000 0.000000
## 42 4 0 0.000000 0.000000
## 43 4 0 0.000000 0.000000
## 44 4 0 0.000000 0.000000
## 45 4 0 0.000000 0.000000
## 46 4 1 0.000000 0.000000
## 47 4 0 0.000000 0.000000
## 48 4 0 0.000000 0.000000
## 49 4 0 0.000000 0.000000
## 50 4 0 0.000000 0.000000
## 51 4 0 0.000000 0.000000
## 52 1 0 0.000000 0.000000
## 53 1 1 0.000000 7.551187
## 54 4 1 0.000000 0.000000
## 55 4 0 0.000000 0.000000
## 56 4 1 0.000000 0.000000
## 57 4 0 0.000000 0.000000
## 58 7 0 0.000000 0.000000
## 59 4 0 0.000000 0.000000
## 60 4 0 8.519989 0.000000
## 61 4 0 7.786552 0.000000
## 62 1 0 0.000000 0.000000
## 63 4 0 0.000000 0.000000
## 64 4 1 0.000000 0.000000
## 65 4 0 0.000000 0.000000
## 66 4 0 0.000000 0.000000
## 67 4 0 0.000000 0.000000
## 68 4 1 0.000000 0.000000
## 69 4 1 0.000000 0.000000
## 70 4 0 0.000000 0.000000
## 71 4 0 0.000000 0.000000
## 72 4 0 0.000000 0.000000
## 73 4 1 0.000000 0.000000
## 74 4 0 0.000000 0.000000
## 75 4 0 0.000000 0.000000
## 76 4 0 0.000000 0.000000
## 77 4 0 0.000000 0.000000
## 78 4 0 0.000000 0.000000
## 79 4 0 0.000000 0.000000
## 80 4 0 0.000000 0.000000
## 81 4 0 0.000000 0.000000
## 82 1 0 0.000000 0.000000
## 83 4 0 0.000000 0.000000
## 84 4 0 0.000000 0.000000
## 85 4 1 9.571157 0.000000
## 86 4 0 0.000000 0.000000
## 87 4 1 0.000000 0.000000
## 88 4 0 0.000000 0.000000
## 89 4 0 0.000000 0.000000
## 90 4 1 0.000000 0.000000
## 91 4 0 0.000000 0.000000
## 92 4 0 0.000000 0.000000
## 93 4 0 0.000000 0.000000
## 94 1 0 0.000000 7.361375
## 95 4 1 0.000000 0.000000
## 96 4 0 0.000000 0.000000
## 97 4 1 0.000000 7.551187
## 98 4 1 0.000000 0.000000
## 99 3 0 0.000000 0.000000
## 100 4 0 0.000000 0.000000
## 101 4 1 0.000000 0.000000
## 102 4 1 9.617471 0.000000
## 103 4 0 0.000000 0.000000
## 104 4 0 0.000000 0.000000
## 105 4 0 0.000000 0.000000
## 106 4 1 8.947546 0.000000
## 107 4 0 10.436935 0.000000
## 108 4 0 0.000000 0.000000
## 109 4 0 0.000000 0.000000
## 110 4 0 0.000000 0.000000
## 111 4 0 0.000000 0.000000
## 112 4 1 0.000000 0.000000
## 113 4 1 0.000000 7.543273
## 114 4 0 8.310169 0.000000
## 115 4 0 0.000000 0.000000
## 116 4 0 0.000000 0.000000
## 117 4 0 0.000000 0.000000
## 118 4 1 0.000000 0.000000
## 119 4 0 0.000000 0.000000
## 120 4 0 0.000000 0.000000
## 121 4 0 0.000000 0.000000
## 122 4 0 0.000000 0.000000
## 123 3 0 0.000000 0.000000
## 124 4 1 0.000000 0.000000
## 125 4 0 0.000000 0.000000
## 126 4 1 0.000000 0.000000
## 127 4 0 0.000000 7.450080
## 128 4 1 0.000000 0.000000
## 129 4 0 0.000000 0.000000
## 130 4 0 0.000000 0.000000
## 131 4 0 0.000000 0.000000
## 132 4 0 0.000000 7.474772
## 133 4 0 8.386401 0.000000
## 134 4 0 0.000000 0.000000
## 135 4 0 0.000000 0.000000
## 136 5 1 0.000000 0.000000
## 137 4 0 0.000000 0.000000
## 138 4 0 0.000000 0.000000
## 139 4 0 0.000000 0.000000
## 140 4 1 0.000000 0.000000
## 141 4 0 0.000000 0.000000
## 142 4 0 0.000000 0.000000
## 143 4 0 0.000000 0.000000
## 144 4 1 0.000000 7.355641
## 145 4 0 0.000000 0.000000
## 146 4 0 0.000000 0.000000
## 147 4 0 0.000000 0.000000
## 148 4 0 0.000000 0.000000
## 149 4 0 0.000000 7.687080
## 150 4 0 0.000000 0.000000
## 151 4 0 0.000000 0.000000
## 152 4 0 0.000000 0.000000
## 153 2 0 0.000000 0.000000
## 154 4 0 0.000000 0.000000
## 155 4 1 0.000000 0.000000
## 156 4 0 0.000000 0.000000
## 157 4 0 0.000000 0.000000
## 158 4 0 0.000000 7.504942
## 159 4 0 0.000000 0.000000
## 160 4 0 0.000000 0.000000
## 161 4 0 0.000000 0.000000
## 162 4 0 0.000000 0.000000
## 163 4 0 0.000000 0.000000
## 164 3 0 0.000000 0.000000
## 165 4 1 0.000000 0.000000
## 166 4 0 0.000000 0.000000
## 167 4 0 0.000000 0.000000
## 168 4 0 0.000000 0.000000
## 169 4 0 0.000000 0.000000
## 170 4 0 0.000000 0.000000
## 171 4 0 0.000000 7.591357
## 172 4 0 0.000000 0.000000
## 173 4 1 0.000000 7.589842
## 174 4 0 0.000000 0.000000
## 175 4 1 0.000000 0.000000
## 176 4 0 0.000000 0.000000
## 177 4 0 0.000000 0.000000
## 178 4 0 0.000000 0.000000
## 179 4 0 0.000000 0.000000
## 180 4 0 0.000000 0.000000
## 181 4 1 0.000000 0.000000
## 182 4 0 0.000000 0.000000
## 183 4 0 0.000000 0.000000
## 184 4 1 0.000000 0.000000
## 185 4 1 0.000000 0.000000
## 186 4 0 0.000000 0.000000
## 187 4 0 0.000000 0.000000
## 188 4 0 0.000000 0.000000
## 189 4 0 0.000000 0.000000
## 190 4 1 0.000000 0.000000
## 191 4 0 0.000000 0.000000
## 192 4 0 0.000000 0.000000
## 193 4 0 0.000000 0.000000
## 194 4 0 0.000000 0.000000
## 195 4 0 0.000000 0.000000
## 196 4 0 0.000000 0.000000
## 197 4 0 0.000000 0.000000
## 198 4 1 9.552866 0.000000
## 199 4 1 8.895493 0.000000
## 200 4 0 0.000000 0.000000
## 201 4 0 0.000000 7.537430
## 202 3 0 0.000000 0.000000
## 203 4 1 0.000000 0.000000
## 204 4 0 0.000000 7.201171
## 205 4 0 0.000000 0.000000
## 206 4 0 0.000000 0.000000
## 207 4 1 8.895493 0.000000
## 208 4 0 0.000000 0.000000
## 209 4 1 9.617471 0.000000
## 210 4 0 0.000000 0.000000
## 211 4 0 0.000000 0.000000
## 212 4 1 0.000000 0.000000
## 213 4 0 0.000000 0.000000
## 214 4 0 0.000000 0.000000
## 215 4 1 0.000000 0.000000
## 216 4 1 0.000000 0.000000
## 217 4 0 0.000000 0.000000
## 218 3 0 0.000000 0.000000
## 219 4 0 0.000000 0.000000
## 220 4 0 0.000000 0.000000
## 221 4 0 0.000000 0.000000
## 222 4 0 0.000000 0.000000
## 223 4 0 0.000000 7.699389
## 224 4 0 0.000000 0.000000
## 225 4 0 0.000000 0.000000
## 226 4 0 0.000000 0.000000
## 227 4 0 0.000000 0.000000
## 228 4 0 7.251345 0.000000
## 229 6 0 0.000000 0.000000
## 230 4 0 0.000000 0.000000
## 231 4 0 8.209308 0.000000
## 232 4 0 0.000000 0.000000
## 233 4 0 0.000000 0.000000
## 234 4 0 0.000000 0.000000
## 235 4 1 0.000000 0.000000
## 236 4 0 0.000000 0.000000
## 237 4 0 0.000000 0.000000
## 238 5 1 0.000000 0.000000
## 239 4 1 0.000000 0.000000
## 240 4 0 0.000000 0.000000
## 241 4 1 0.000000 0.000000
## 242 4 0 0.000000 0.000000
## 243 4 0 0.000000 0.000000
## 244 4 0 0.000000 0.000000
## 245 4 0 0.000000 7.462789
## 246 1 0 0.000000 0.000000
## 247 4 1 0.000000 0.000000
## 248 4 0 0.000000 0.000000
## 249 4 1 0.000000 7.589842
## 250 1 0 0.000000 0.000000
## 251 4 1 0.000000 0.000000
## 252 4 0 0.000000 0.000000
## 253 4 0 0.000000 0.000000
## 254 4 0 0.000000 0.000000
## 255 4 0 0.000000 0.000000
## 256 2 1 0.000000 0.000000
## 257 4 0 0.000000 0.000000
## 258 4 0 0.000000 0.000000
## 259 4 0 0.000000 0.000000
## 260 4 0 0.000000 0.000000
## 261 4 0 0.000000 0.000000
## 262 4 0 0.000000 0.000000
## 263 4 0 0.000000 0.000000
## 264 4 0 8.310169 0.000000
## 265 4 0 0.000000 0.000000
## 266 2 1 0.000000 0.000000
## 267 4 0 0.000000 0.000000
## 268 4 1 0.000000 0.000000
## 269 4 0 0.000000 0.000000
## 270 4 1 0.000000 0.000000
## 271 4 1 0.000000 0.000000
## 272 4 0 6.962243 0.000000
## 273 4 0 7.786552 0.000000
## 274 4 0 0.000000 0.000000
## 275 4 0 0.000000 0.000000
## 276 4 0 0.000000 0.000000
## 277 4 1 8.895493 0.000000
## 278 4 0 0.000000 0.000000
## 279 4 0 0.000000 0.000000
## 280 4 1 8.895493 0.000000
## 281 3 0 0.000000 0.000000
## 282 4 1 8.552367 0.000000
## 283 4 0 0.000000 0.000000
## 284 4 0 0.000000 0.000000
## 285 4 0 0.000000 0.000000
## 286 4 1 9.617471 0.000000
## 287 4 1 0.000000 0.000000
## 288 4 0 0.000000 0.000000
## 289 4 0 0.000000 0.000000
## 290 6 0 0.000000 0.000000
## 291 4 0 0.000000 0.000000
## 292 4 0 0.000000 0.000000
## 293 4 0 0.000000 0.000000
## 294 4 0 0.000000 0.000000
## 295 4 0 0.000000 0.000000
## 296 4 0 0.000000 0.000000
## 297 4 0 0.000000 0.000000
## 298 1 0 8.150468 0.000000
## 299 4 0 0.000000 0.000000
## 300 4 0 0.000000 0.000000
## 301 4 1 8.947546 0.000000
## 302 4 0 0.000000 0.000000
## 303 4 1 0.000000 0.000000
## 304 4 1 8.386401 0.000000
## 305 2 0 0.000000 0.000000
## 306 4 0 0.000000 0.000000
## 307 4 0 0.000000 0.000000
## 308 4 0 0.000000 0.000000
## 309 4 1 0.000000 0.000000
## 310 4 1 0.000000 0.000000
## 311 4 0 0.000000 0.000000
## 312 2 1 8.895493 0.000000
## 313 4 0 0.000000 0.000000
## 314 4 1 0.000000 0.000000
## 315 4 0 0.000000 0.000000
## 316 4 0 0.000000 0.000000
## 317 4 0 0.000000 0.000000
## 318 4 0 0.000000 0.000000
## 319 4 0 0.000000 0.000000
## 320 4 0 0.000000 0.000000
## 321 4 0 0.000000 0.000000
## 322 4 0 0.000000 7.303843
## 323 4 0 0.000000 0.000000
## 324 4 0 0.000000 0.000000
## 325 4 0 0.000000 0.000000
## 326 4 0 0.000000 0.000000
## 327 4 0 0.000000 0.000000
## 328 4 1 0.000000 7.543273
## 329 4 0 0.000000 0.000000
## 330 4 0 7.626083 0.000000
## 331 4 0 0.000000 0.000000
## 332 4 0 0.000000 0.000000
## 333 4 0 0.000000 0.000000
## 334 4 0 0.000000 0.000000
## 335 4 0 0.000000 0.000000
## 336 4 0 0.000000 0.000000
## 337 4 0 0.000000 0.000000
## 338 4 0 0.000000 0.000000
## 339 1 0 0.000000 0.000000
## 340 4 0 0.000000 0.000000
## 341 4 0 0.000000 0.000000
## 342 4 1 8.895493 0.000000
## 343 4 1 8.895493 0.000000
## 344 4 0 0.000000 0.000000
## 345 4 0 0.000000 0.000000
## 346 4 0 0.000000 0.000000
## 347 4 0 0.000000 0.000000
## 348 4 0 0.000000 0.000000
## 349 5 0 0.000000 0.000000
## 350 4 0 0.000000 0.000000
## 351 4 1 0.000000 0.000000
## 352 4 0 0.000000 0.000000
## 353 4 1 0.000000 0.000000
## 354 4 0 0.000000 0.000000
## 355 4 1 0.000000 0.000000
## 356 4 1 0.000000 0.000000
## 357 4 0 0.000000 0.000000
## 358 4 1 0.000000 0.000000
## 359 4 0 0.000000 0.000000
## 360 3 0 0.000000 0.000000
## 361 4 0 0.000000 0.000000
## 362 4 1 0.000000 0.000000
## 363 4 0 0.000000 0.000000
## 364 4 1 0.000000 7.355641
## 365 4 0 0.000000 0.000000
## 366 4 0 0.000000 0.000000
## 367 4 0 0.000000 0.000000
## 368 4 0 0.000000 0.000000
## 369 4 1 0.000000 0.000000
## 370 4 0 0.000000 0.000000
## 371 4 0 0.000000 0.000000
## 372 4 0 0.000000 0.000000
## 373 4 1 0.000000 0.000000
## 374 4 0 0.000000 0.000000
## 375 4 0 0.000000 0.000000
## 376 4 0 0.000000 0.000000
## 377 4 0 0.000000 0.000000
## 378 4 0 0.000000 0.000000
## 379 4 0 0.000000 0.000000
## 380 4 0 0.000000 0.000000
## 381 4 0 0.000000 0.000000
## 382 4 0 0.000000 7.757906
## 383 4 0 0.000000 0.000000
## 384 4 0 0.000000 0.000000
## 385 4 0 0.000000 0.000000
## 386 4 0 0.000000 0.000000
## 387 4 0 0.000000 0.000000
## 388 4 1 0.000000 7.789869
## 389 4 0 0.000000 0.000000
## 390 4 0 0.000000 0.000000
## 391 4 1 0.000000 0.000000
## 392 4 0 0.000000 0.000000
## 393 4 0 0.000000 0.000000
## 394 1 1 8.947546 0.000000
## 395 4 0 0.000000 0.000000
## 396 4 0 0.000000 0.000000
## 397 4 0 0.000000 0.000000
## 398 4 0 0.000000 0.000000
## 399 4 1 0.000000 0.000000
## 400 4 1 0.000000 0.000000
## 401 4 0 0.000000 0.000000
## 402 4 0 0.000000 0.000000
## 403 4 1 0.000000 0.000000
## 404 4 0 0.000000 0.000000
## 405 7 0 0.000000 7.687080
## 406 4 1 0.000000 7.589842
## 407 4 0 0.000000 0.000000
## 408 4 0 0.000000 0.000000
## 409 4 1 0.000000 7.543273
## 410 4 0 0.000000 0.000000
## 411 4 0 0.000000 0.000000
## 412 4 0 0.000000 0.000000
## 413 4 0 0.000000 0.000000
## 414 4 1 9.617471 0.000000
## 415 4 0 0.000000 0.000000
## 416 4 1 0.000000 0.000000
## 417 4 0 0.000000 0.000000
## 418 4 0 0.000000 0.000000
## 419 4 0 0.000000 0.000000
## 420 4 0 0.000000 0.000000
## 421 4 0 0.000000 0.000000
## 422 4 0 7.685703 0.000000
## 423 4 1 0.000000 0.000000
## 424 4 0 0.000000 0.000000
## 425 4 0 0.000000 0.000000
## 426 4 0 0.000000 0.000000
## 427 4 0 0.000000 7.250636
## 428 1 0 0.000000 0.000000
## 429 4 1 0.000000 0.000000
## 430 4 0 0.000000 0.000000
## 431 4 0 0.000000 0.000000
## 432 4 0 0.000000 0.000000
## 433 4 0 7.684784 0.000000
## 434 4 1 0.000000 0.000000
## 435 2 0 0.000000 7.591357
## 436 4 1 0.000000 0.000000
## 437 4 0 6.388561 0.000000
## 438 4 0 0.000000 0.000000
## 439 4 0 0.000000 0.000000
## 440 4 0 0.000000 0.000000
## 441 4 0 0.000000 0.000000
## 442 4 0 0.000000 0.000000
## 443 4 0 0.000000 0.000000
## 444 4 1 0.000000 0.000000
## 445 4 0 0.000000 0.000000
## 446 4 0 0.000000 0.000000
## 447 4 0 0.000000 0.000000
## 448 4 0 0.000000 0.000000
## 449 4 0 0.000000 0.000000
## 450 4 0 0.000000 0.000000
## 451 4 0 0.000000 0.000000
## 452 4 1 0.000000 0.000000
## 453 4 1 0.000000 0.000000
## 454 1 1 0.000000 0.000000
## 455 4 1 0.000000 0.000000
## 456 4 1 0.000000 0.000000
## 457 4 0 0.000000 0.000000
## 458 4 0 0.000000 0.000000
## 459 4 0 0.000000 0.000000
## 460 4 0 0.000000 0.000000
## 461 4 0 0.000000 0.000000
## 462 4 0 0.000000 0.000000
## 463 4 0 0.000000 0.000000
## 464 4 0 0.000000 0.000000
## 465 4 0 0.000000 0.000000
## 466 4 0 0.000000 0.000000
## 467 4 0 0.000000 0.000000
## 468 4 0 0.000000 0.000000
## 469 4 1 0.000000 7.589842
## 470 4 1 0.000000 0.000000
## 471 4 1 0.000000 0.000000
## 472 4 0 0.000000 0.000000
## 473 4 0 0.000000 0.000000
## 474 4 0 0.000000 0.000000
## 475 4 0 0.000000 0.000000
## 476 4 0 0.000000 0.000000
## 477 4 0 0.000000 0.000000
## 478 4 0 0.000000 0.000000
## 479 4 0 0.000000 0.000000
## 480 4 0 0.000000 0.000000
## 481 3 0 0.000000 0.000000
## 482 4 0 0.000000 0.000000
## 483 4 0 0.000000 0.000000
## 484 4 0 0.000000 0.000000
## 485 4 0 6.388561 0.000000
## 486 4 0 0.000000 0.000000
## 487 4 0 0.000000 0.000000
## 488 4 0 0.000000 0.000000
## 489 4 0 0.000000 0.000000
## 490 4 1 9.617471 0.000000
## 491 4 0 0.000000 0.000000
## 492 4 0 0.000000 0.000000
## 493 4 0 0.000000 0.000000
## 494 4 0 0.000000 0.000000
## 495 4 0 0.000000 0.000000
## 496 4 0 0.000000 0.000000
## 497 4 0 0.000000 0.000000
## 498 1 0 0.000000 0.000000
## 499 4 0 0.000000 0.000000
## 500 4 0 0.000000 0.000000
## 501 3 0 0.000000 0.000000
## 502 4 0 0.000000 0.000000
## 503 4 0 0.000000 0.000000
## 504 3 0 0.000000 0.000000
## 505 4 0 0.000000 0.000000
## 506 4 0 0.000000 0.000000
## 507 4 0 0.000000 0.000000
## 508 4 0 0.000000 0.000000
## 509 4 0 0.000000 0.000000
## 510 4 0 0.000000 0.000000
## 511 4 0 0.000000 7.230563
## 512 4 0 0.000000 0.000000
## 513 4 0 0.000000 0.000000
## 514 4 1 0.000000 0.000000
## 515 4 0 0.000000 0.000000
## 516 4 0 0.000000 0.000000
## 517 4 0 0.000000 0.000000
## 518 4 0 0.000000 0.000000
## 519 4 0 0.000000 0.000000
## 520 4 1 0.000000 0.000000
## 521 4 1 9.617471 0.000000
## 522 4 1 8.947546 0.000000
## 523 4 0 0.000000 0.000000
## 524 4 1 0.000000 0.000000
## 525 4 1 0.000000 0.000000
## 526 4 0 0.000000 0.000000
## 527 4 1 0.000000 0.000000
## 528 4 0 0.000000 0.000000
## 529 4 0 0.000000 0.000000
## 530 4 0 0.000000 0.000000
## 531 7 0 0.000000 0.000000
## 532 4 0 0.000000 0.000000
## 533 4 0 0.000000 0.000000
## 534 4 1 0.000000 0.000000
## 535 4 1 9.906084 0.000000
## 536 4 0 0.000000 0.000000
## 537 4 0 0.000000 0.000000
## 538 4 0 0.000000 0.000000
## 539 4 0 0.000000 0.000000
## 540 4 0 0.000000 0.000000
## 541 4 0 0.000000 0.000000
## 542 4 0 0.000000 0.000000
## 543 4 0 0.000000 0.000000
## 544 4 0 0.000000 0.000000
## 545 4 0 0.000000 0.000000
## 546 4 0 0.000000 0.000000
## 547 4 0 0.000000 0.000000
## 548 4 0 0.000000 0.000000
## 549 4 0 0.000000 0.000000
## 550 4 0 0.000000 0.000000
## 551 4 0 0.000000 0.000000
## 552 4 0 0.000000 0.000000
## 553 4 0 0.000000 0.000000
## 554 4 0 0.000000 0.000000
## 555 4 1 0.000000 0.000000
## 556 4 0 0.000000 0.000000
## 557 4 0 0.000000 0.000000
## 558 1 0 0.000000 0.000000
## 559 4 0 0.000000 0.000000
## 560 4 0 0.000000 0.000000
## 561 4 0 0.000000 0.000000
## 562 4 1 0.000000 0.000000
## 563 4 1 0.000000 0.000000
## 564 4 0 0.000000 0.000000
## 565 4 1 0.000000 0.000000
## 566 4 0 0.000000 0.000000
## 567 4 0 8.519989 0.000000
## 568 4 0 0.000000 0.000000
## 569 4 0 0.000000 0.000000
## 570 4 0 0.000000 0.000000
## 571 4 0 0.000000 0.000000
## 572 4 0 0.000000 0.000000
## 573 4 0 0.000000 0.000000
## 574 4 1 0.000000 7.589842
## 575 4 0 0.000000 0.000000
## 576 4 1 0.000000 0.000000
## 577 4 0 0.000000 0.000000
## 578 4 0 0.000000 0.000000
## 579 4 0 0.000000 0.000000
## 580 4 0 0.000000 0.000000
## 581 4 1 0.000000 0.000000
## 582 4 1 0.000000 0.000000
## 583 4 0 0.000000 0.000000
## 584 4 0 0.000000 0.000000
## 585 4 0 0.000000 0.000000
## 586 4 0 0.000000 0.000000
## 587 4 0 0.000000 0.000000
## 588 4 0 0.000000 0.000000
## 589 4 1 0.000000 7.543273
## 590 4 0 0.000000 0.000000
## 591 4 0 0.000000 0.000000
## 592 4 0 0.000000 0.000000
## 593 4 1 0.000000 0.000000
## 594 4 1 8.947546 0.000000
## 595 4 0 0.000000 0.000000
## 596 4 0 0.000000 0.000000
## 597 4 0 0.000000 0.000000
## 598 4 1 0.000000 7.551187
## 599 4 0 7.684784 0.000000
## 600 4 0 0.000000 0.000000
## 601 4 0 0.000000 0.000000
## 602 4 0 0.000000 0.000000
## 603 4 0 0.000000 0.000000
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## [ reached 'max' / getOption("max.print") -- omitted 25420 rows ]# Fit logistic regression model
model2 <- glm(income ~ ., family = binomial, data = adult_dum)
# Calculate VIF
vif_values <- vif(model2)
vif_values## age workclass_num education_level_num years_education
## 1.064021 1.104549 1.497421 1.586600
## marital_status_num occupation_num household_role_num race_num
## 1.026590 1.198104 1.640140 1.063348
## gender_num hours_per_week native_region_num capital_gain_log
## 1.648998 1.075349 1.062321 1.021075
## capital_loss_log
## 1.008989# Visualize correlation matrix of predictors
# Visualize correlation matrix of predictors
cor_matrix <- cor(dplyr::select(adult_dum, -income))
corrplot(cor_matrix, method='circle')
Assumption 4: Check for Extreme Outliers with high influence
- There are 28 outliers that need to be removed.
- Note: Data points with an absolute standardized residuals above 3 represent possible outliers and may deserve closer attention.
library(broom)
model <- glm(income ~ ., data = adult2, family = binomial)
plot(model, which = 4, id.n = 10)
# Extract model results
model.data <- augment(model) %>%
dplyr::mutate(index = dplyr::row_number()) # Use row_number() instead of n()
# Show the top 5
model.data %>% top_n(10, .cooksd)## # A tibble: 10 × 21
## income age workclass education_level years_education marital_status
## <fct> <dbl> <fct> <fct> <dbl> <fct>
## 1 >50K 42 LocGov highsch 7 divorce
## 2 >50K 61 Private highsch 5 divorce
## 3 >50K 36 Private undergrad 15 notmarried
## 4 >50K 51 SelfEmpNotInc assoc 10 divorce
## 5 <=50K 51 Private phd 16 married
## 6 >50K 46 SelfEmpNotInc undergrad 13 divorce
## 7 >50K 42 Private highsch 6 married
## 8 >50K 53 Private primary 3 married
## 9 >50K 27 SelfEmpNotInc assoc 10 married
## 10 >50K 22 LocGov primary 3 notmarried
## # ℹ 15 more variables: occupation <fct>, household_role <fct>, race <fct>,
## # gender <fct>, hours_per_week <dbl>, native_region <fct>,
## # capital_gain_log <dbl>, capital_loss_log <dbl>, .fitted <dbl>,
## # .resid <dbl>, .hat <dbl>, .sigma <dbl>, .cooksd <dbl>, .std.resid <dbl>,
## # index <int># Cook's D influencial outliers
ggplot(model.data, aes(index, .std.resid)) +
geom_point(aes(color = income), alpha = 0.5) +
theme_bw()
# Influence Plot and list of top influencers - Continuous
influencePlot(model, id=list(method="noteworthy", n=5, cex=.75, col=carPalette()[1], location="lr"),
main="Influence Plot", sub="Circle area is proportial to Cook's Distance - Continuous" )
## StudRes Hat CookD
## 5386 3.112180440 1.247824e-03 3.323438e-03
## 7742 3.314233371 1.800507e-04 9.687007e-04
## 10459 2.951390005 1.945303e-03 3.179724e-03
## 11135 -0.004594035 2.593294e-01 9.648228e-08
## 12531 3.228367668 1.301133e-04 5.329337e-04
## 18537 1.922555010 3.259376e-02 3.838740e-03
## 20074 -0.002941657 3.464054e-01 6.303461e-08
## 23274 3.327380266 9.455825e-05 5.365417e-04
## 24597 -0.001745949 1.425239e-01 6.199444e-09
## 25150 3.436255742 1.390405e-04 1.126862e-03
## 31369 3.012334359 2.259192e-03 4.329866e-03
## 32215 3.686489185 1.310298e-04 2.515152e-03
## 32263 -0.001676829 1.323018e-01 5.216935e-09
## 32305 -0.007845815 9.200691e-01 1.491191e-05# Remove influential data points
model.data %>%
filter(abs(.std.resid) > 3)## # A tibble: 15 × 21
## income age workclass education_level years_education marital_status
## <fct> <dbl> <fct> <fct> <dbl> <fct>
## 1 >50K 52 Private highsch 9 widowed
## 2 >50K 61 Private highsch 5 divorce
## 3 >50K 19 Private secondary 4 notmarried
## 4 >50K 29 Private assoc 10 notmarried
## 5 >50K 39 Private highsch 9 notmarried
## 6 >50K 28 Private highsch 5 notmarried
## 7 >50K 61 StateGov highsch 7 widowed
## 8 >50K 24 Private highsch 9 divorce
## 9 >50K 27 LocGov assoc 10 notmarried
## 10 >50K 54 Private highsch 7 divorce
## 11 >50K 27 StateGov highsch 9 notmarried
## 12 >50K 22 Private assoc 10 notmarried
## 13 >50K 33 Private secondary 4 notmarried
## 14 >50K 27 Private highsch 9 notmarried
## 15 >50K 22 LocGov primary 3 notmarried
## # ℹ 15 more variables: occupation <fct>, household_role <fct>, race <fct>,
## # gender <fct>, hours_per_week <dbl>, native_region <fct>,
## # capital_gain_log <dbl>, capital_loss_log <dbl>, .fitted <dbl>,
## # .resid <dbl>, .hat <dbl>, .sigma <dbl>, .cooksd <dbl>, .std.resid <dbl>,
## # index <int># Remove influential data points based on standardized residuals
adult.out <- adult2 %>%
slice(-which(abs(model.data$.std.resid) > 3))
model_out <- glm(income ~ ., data = adult.out, family = binomial)
plot(model_out, which = 4, id.n = 10)
# Influence Plot and list of top influencers - Continuous
influencePlot(model_out, id=list(method="noteworthy", n=5, cex=.75, col=carPalette()[1], location="lr"),
main="Influence Plot", sub="Circle area is proportial to Cook's Distance - Continuous" )
## StudRes Hat CookD
## 4807 1.983148978 0.0198998771 2.702839e-03
## 10455 2.996330504 0.0018273002 3.402185e-03
## 11131 -0.004656880 0.2641420672 1.019222e-07
## 12805 3.016212551 0.0005107485 1.061303e-03
## 18454 2.999490886 0.0013090848 2.507563e-03
## 18528 1.984936996 0.0312428704 4.203757e-03
## 20064 -0.002968960 0.3502876077 6.547146e-08
## 23946 2.990612371 0.0002361915 4.599334e-04
## 24585 -0.001751131 0.1428122273 6.251995e-09
## 31356 3.008506119 0.0023031492 4.360378e-03
## 32248 -0.001685655 0.1331326041 5.312552e-09
## 32290 -0.008195518 0.9260482958 1.779922e-05Assumption 5: There is a Linear Relationship Between Explanatory (continuous) Variables and the Logit (log odds) of the Response Variable
- For categorical predictors, the concept of linearity doesn’t apply in the traditional sense because these variables are represented in the model as a series of dummy variables (one for each category, minus one reference category).
- Because the dependent variable is categorical and any transformation to dummy variables would not be an accurate assessment of a linear relationship, you don’t have to do this assumptions check on the categorical. Below are just some tests.
- Sources: https://bookdown.org/sarahwerth2024/CategoricalBook/logistic-regression-r.html
library(tidyverse)
library(broom)
# Fit the logistic regression model
model <- glm(income ~ ., data = adult_dum, family = binomial)
# Predict the probability (p) of diabetes positivity
probabilities <- predict(model, type = "response")
# Create binary predicted classes based on a threshold (e.g., 0.5)
predicted_classes <- ifelse(probabilities > 0.24, "0", "1")
# Visualize the relationship between a continuous predictor (e.g., age) and logit values
logit_values <- log(probabilities / (1 - probabilities))
# All One-Hot-Encoded variables
ggplot(adult_dum,(aes(x=logit_values,y=income)))+geom_point()+geom_smooth(method="loess",span=.9)## `geom_smooth()` using formula = 'y ~ x'
# logit against age
ggplot(adult_dum,(aes(x=logit_values,y=age)))+geom_point()+geom_smooth(method="loess",span=.9)## `geom_smooth()` using formula = 'y ~ x'
# logit against years_education
ggplot(adult_dum,(aes(x=logit_values,y=years_education)))+geom_point()+geom_smooth(method="loess",span=.9)## `geom_smooth()` using formula = 'y ~ x'
# Check for linearity in continuous variables
boxTidwell(income ~ age + years_education, data = adult_dum)## Warning in boxTidwell.default(y, X1, X2, max.iter = max.iter, tol = tol, :
## maximum iterations exceeded## MLE of lambda Score Statistic (t) Pr(>|t|)
## age -1.1111 -29.978 < 2.2e-16 ***
## years_education 3.0465 19.918 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## iterations = 26
##
## Score test for null hypothesis that all lambdas = 1:
## F = 660.28, df = 2 and 32557, Pr(>F) = < 2.2e-16Post EDA/assumption check data-split to train and test sets
# Set a seed for reproducibility
set.seed(1234)
# Calculate the number of rows to select
sample_size <- round(0.8 * nrow(adult2))
# Create a random sample of indices
index <- sample(1:nrow(adult2), size = sample_size, replace = FALSE)
# Create the training set using the selected indices
training_set <- adult2[index, ]
# Create the testing set with the remaining indices
testing_set <- adult2[-index, ]
nrow(training_set)## [1] 26050nrow(testing_set)## [1] 6512testing_set## age workclass education_level years_education marital_status
## 4 53 Private highsch 7 married
## 5 28 Private undergrad 13 married
## 8 52 SelfEmpNotInc highsch 9 married
## 9 31 Private master 14 notmarried
## 11 37 Private assoc 10 married
## 12 30 StateGov undergrad 13 married
## 17 25 SelfEmpNotInc highsch 9 notmarried
## 19 38 Private highsch 7 married
## 21 40 Private phd 16 married
## 27 19 Private highsch 9 notmarried
## 33 45 Private undergrad 13 divorce
## 37 21 Private assoc 10 notmarried
## 39 31 Private assoc 10 married
## 46 57 FedGov undergrad 13 married
## 47 53 Private highsch 9 married
## 52 18 Private highsch 9 notmarried
## 55 47 SelfEmpInc highsch 9 divorce
## 56 43 Private assoc 10 married
## 62 32 unemployed secondary 4 married
## 66 36 Private highsch 9 married
## 70 25 unemployed assoc 10 notmarried
## 71 19 Private assoc 10 notmarried
## 72 31 Private undergrad 13 divorce
## 78 67 unemployed highsch 6 married
## 89 30 Private highsch 5 notmarried
## 90 43 FedGov phd 16 notmarried
## 93 28 Private assoc 10 divorce
## 96 29 LocGov assoc 10 notmarried
## 98 37 Private assoc 10 married
## 99 48 Private assoc 12 divorce
## 104 20 Private assoc 10 notmarried
## 112 38 Private undergrad 15 married
## 114 28 Private assoc 10 married
## 116 53 Private highsch 5 married
## 117 56 SelfEmpInc assoc 10 married
## 128 31 Private assoc 12 married
## 132 24 Private highsch 9 notmarried
## 136 38 FedGov master 14 married
## 142 27 Private assoc 10 notmarried
## 143 19 Private assoc 10 notmarried
## 151 47 LocGov assoc 10 divorce
## 154 21 Private highsch 9 notmarried
## 155 52 unemployed highsch 9 divorce
## 166 36 Private assoc 10 divorce
## 168 46 StateGov master 14 widowed
## 178 34 Private highsch 9 notmarried
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## 31562 57 SelfEmpNotInc phd 16 married
## 31569 28 unemployed highsch 9 notmarried
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## 31580 38 Private highsch 9 notmarried
## 31587 49 Private highsch 9 divorce
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## 31726 30 Private assoc 10 divorce
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## 31760 18 Private highsch 9 notmarried
## 31761 25 Private highsch 9 notmarried
## 31762 44 SelfEmpInc undergrad 13 married
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## 31771 37 Private highsch 9 divorce
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## 31814 44 Private undergrad 13 married
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## 31838 31 Private assoc 12 notmarried
## 31840 28 Private assoc 12 notmarried
## 31846 43 Private assoc 10 married
## 31852 42 SelfEmpNotInc undergrad 15 divorce
## 31864 40 SelfEmpNotInc undergrad 13 married
## 31867 34 Private highsch 9 notmarried
## 31868 18 Private highsch 6 notmarried
## 31880 27 Private highsch 9 divorce
## 31885 41 SelfEmpInc master 14 married
## 31886 35 unemployed assoc 10 notmarried
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## 32009 58 unemployed primary 2 married
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## 32025 53 Private highsch 9 married
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## 32028 46 LocGov master 14 divorce
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## 32131 38 SelfEmpNotInc master 14 married
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## 32153 22 Private highsch 7 divorce
## 32155 34 Private highsch 9 married
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## 32161 23 Private assoc 10 notmarried
## 32166 51 Private assoc 10 notmarried
## 32167 42 LocGov undergrad 13 divorce
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## 32189 29 Private undergrad 13 notmarried
## 32193 64 StateGov assoc 10 divorce
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## 32259 51 SelfEmpInc undergrad 15 married
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## 32266 37 LocGov undergrad 13 married
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## 32526 81 unemployed assoc 11 divorce
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## 32558 40 Private highsch 9 married
## occupation household_role race gender hours_per_week
## 4 lowskill husband black male 40
## 5 highskill wife black female 40
## 8 highskill husband white male 45
## 9 highskill tenant white female 50
## 11 highskill husband black male 80
## 12 highskill husband Asian_Pac_Islander male 40
## 17 agriculture depchild white male 35
## 19 highskill husband white male 50
## 21 highskill husband white male 60
## 27 midskill depchild white male 40
## 33 highskill depchild white male 40
## 37 midskill depchild white male 40
## 39 highskill husband white male 38
## 46 highskill husband black male 40
## 47 midskill husband white male 38
## 52 lowskill depchild white female 30
## 55 highskill tenant white male 60
## 56 highskill husband white male 40
## 62 other tenant white male 40
## 66 midskill husband white male 40
## 70 other depchild white male 40
## 71 highskill depchild white male 32
## 72 highskill depchild black female 40
## 78 other husband white male 2
## 89 highskill tenant white male 40
## 90 highskill tenant white female 50
## 93 midskill single_occ black female 25
## 96 lowskill tenant white male 50
## 98 highskill husband white male 48
## 99 highskill single_occ white female 40
## 104 highskill depchild white female 40
## 112 highskill husband white male 40
## 114 highskill wife white female 25
## 116 lowskill husband white male 50
## 117 highskill husband white male 50
## 128 clerical husband white male 40
## 132 lowskill tenant white female 40
## 136 highskill husband white male 40
## 142 highskill depchild white female 40
## 143 lowskill depchild white male 30
## 151 clerical single_occ white female 38
## 154 midskill depchild white male 35
## 155 other tenant white male 45
## 166 lowskill single_occ white female 40
## 168 highskill single_occ white male 40
## 178 lowskill tenant black male 40
## 190 highskill husband white male 1
## 197 midskill tenant white male 40
## 201 highskill tenant white female 40
## 205 clerical single_occ white female 35
## 212 midskill husband white male 40
## 213 highskill tenant native female 40
## 223 lowskill tenant black male 40
## 226 highskill depchild white female 40
## 240 midskill depchild white male 40
## 243 highskill husband white male 50
## 245 midskill tenant white male 40
## 246 clerical husband white male 35
## 247 clerical husband white male 45
## 255 midskill tenant white male 20
## 257 lowskill depchild white female 40
## 266 highskill husband Asian_Pac_Islander male 75
## 268 highskill husband white male 40
## 276 lowskill husband black male 40
## 281 highskill tenant white female 15
## 283 highskill single_occ black female 45
## 288 lowskill tenant white male 45
## 295 highskill single_occ black female 40
## 297 highskill tenant white female 60
## 299 midskill depchild white male 40
## 306 clerical tenant white female 40
## 309 clerical husband white male 40
## 311 highskill tenant white male 40
## 314 highskill tenant black female 40
## 323 midskill tenant white male 45
## 326 agriculture husband white male 45
## 333 lowskill tenant white male 40
## 335 midskill husband white male 40
## 342 midskill husband white male 40
## 343 highskill husband white male 40
## 354 midskill husband white male 30
## 355 other husband white male 40
## 356 clerical husband white male 40
## 357 lowskill single_occ other male 40
## 360 midskill husband white male 40
## 371 clerical single_occ white female 15
## 373 midskill husband white male 38
## 375 midskill tenant black male 40
## 381 midskill tenant white male 40
## 384 highskill tenant white female 40
## 388 clerical wife white female 6
## 392 midskill tenant white male 50
## 395 lowskill tenant white female 35
## 404 highskill tenant white female 64
## 408 midskill husband white male 25
## 409 other wife white female 40
## 410 highskill husband white male 50
## 418 lowskill depchild white female 19
## 422 lowskill depchild white male 18
## 423 highskill wife white female 40
## 432 other depchild white male 40
## 444 highskill husband white male 40
## 453 midskill husband white male 40
## 455 highskill wife white female 40
## 456 midskill husband Asian_Pac_Islander male 40
## 462 midskill husband black male 40
## 468 midskill husband white male 40
## 470 highskill wife white female 16
## 477 lowskill tenant white male 6
## 478 lowskill depchild white male 30
## 480 lowskill single_occ white female 35
## 496 clerical single_occ white female 36
## 498 lowskill depchild white male 40
## 502 midskill husband white male 40
## 504 lowskill single_occ white female 42
## 508 highskill husband white male 40
## 511 lowskill single_occ white male 42
## 512 other single_occ black female 30
## 524 highskill husband white male 45
## 526 highskill wife white female 40
## 527 highskill husband white male 40
## 530 highskill husband white male 40
## 533 lowskill husband black male 40
## 534 highskill wife white female 50
## 535 highskill husband white male 48
## 536 highskill husband white male 50
## 538 midskill tenant white male 40
## 542 highskill depchild white male 40
## 544 midskill tenant white male 40
## 547 lowskill wife white female 8
## 552 lowskill single_occ white female 40
## 553 clerical single_occ white female 40
## 560 lowskill husband white male 40
## 565 highskill husband white male 60
## 576 highskill wife white male 40
## 579 highskill tenant white female 40
## 580 midskill husband white male 45
## 583 midskill husband white male 40
## 584 midskill husband white male 40
## 587 clerical tenant white male 60
## 590 highskill tenant white male 80
## 594 highskill husband white male 40
## 598 highskill husband white male 50
## 603 lowskill tenant white female 40
## 632 lowskill single_occ black female 40
## 637 lowskill tenant white male 40
## 638 highskill husband white male 60
## 641 midskill tenant white male 55
## 643 lowskill depchild white male 40
## 652 highskill relative white male 50
## 653 highskill husband Asian_Pac_Islander male 40
## 654 highskill tenant white female 45
## 660 midskill husband white male 40
## 670 midskill tenant white male 40
## 672 other depchild white female 20
## 681 highskill depchild white male 48
## 683 midskill husband white male 40
## 688 highskill wife white female 55
## 701 highskill tenant white male 50
## 711 highskill husband white male 40
## 723 highskill tenant white male 40
## 724 highskill husband white male 42
## 727 highskill wife white female 40
## 728 lowskill tenant white female 20
## 730 highskill husband white male 59
## 736 midskill husband white male 40
## 747 midskill husband white male 40
## 748 clerical depchild black female 40
## 755 lowskill tenant white male 40
## 764 highskill husband white male 70
## 774 highskill wife white female 24
## 781 midskill husband white male 40
## 785 highskill tenant other female 55
## 787 agriculture husband white male 50
## 788 midskill depchild white male 30
## 789 other husband white male 16
## 800 highskill husband white male 40
## 802 lowskill depchild black male 30
## 813 highskill tenant white male 45
## 818 midskill husband white male 50
## 826 midskill single_occ white male 42
## 830 highskill depchild white male 40
## 832 clerical wife white female 40
## 834 highskill husband white male 40
## 835 other tenant white female 24
## 839 highskill tenant white male 50
## 842 midskill husband white male 50
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## 852 clerical tenant white female 55
## 853 highskill husband white male 50
## 855 highskill depchild white male 50
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## 860 highskill tenant white female 56
## 862 agriculture husband white male 40
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## 871 highskill husband white male 40
## 873 highskill husband white male 40
## 881 midskill tenant white female 40
## 889 highskill husband black male 40
## 894 highskill husband white male 50
## 895 agriculture husband white male 65
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## 899 highskill husband white male 45
## 907 midskill tenant white male 52
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## 909 lowskill depchild white male 10
## 910 highskill husband white male 40
## 919 highskill husband white male 30
## 922 midskill single_occ white male 40
## 929 highskill tenant white female 42
## 939 midskill husband white male 30
## 940 highskill tenant white male 40
## 942 midskill husband black male 40
## 944 lowskill tenant white female 45
## 952 highskill husband white male 43
## 963 midskill husband white male 70
## 964 highskill single_occ white female 40
## 972 highskill tenant white female 35
## 975 midskill single_occ white male 70
## 976 highskill tenant white female 40
## 978 highskill husband white male 35
## 1008 highskill husband white male 40
## 1010 highskill husband white male 50
## 1011 midskill tenant white female 40
## 1019 midskill tenant white male 40
## 1027 highskill tenant black male 45
## 1034 lowskill tenant black female 40
## 1038 lowskill tenant white male 75
## 1044 highskill husband white male 60
## 1049 highskill tenant white female 50
## 1053 clerical husband white male 40
## 1056 highskill husband white male 40
## 1065 highskill husband white male 80
## 1072 midskill husband white male 40
## 1085 clerical husband white male 40
## 1087 highskill husband white male 40
## 1090 midskill depchild white male 28
## 1099 highskill wife white female 25
## 1102 midskill husband white male 60
## 1107 highskill tenant white male 47
## 1110 midskill husband white male 55
## 1114 highskill wife white female 40
## 1117 midskill husband white male 40
## 1118 midskill husband white male 50
## 1120 highskill husband Asian_Pac_Islander male 40
## 1121 highskill husband white male 40
## 1122 highskill tenant white female 40
## 1124 highskill husband white male 40
## 1129 lowskill single_occ white male 45
## 1148 midskill husband white male 50
## 1149 highskill husband white male 35
## 1151 lowskill depchild white male 30
## 1158 highskill wife white female 50
## 1171 clerical tenant white female 43
## 1173 agriculture tenant white male 99
## 1180 lowskill tenant Asian_Pac_Islander male 45
## 1189 clerical tenant white female 65
## 1204 lowskill tenant white male 40
## 1211 highskill husband white male 60
## 1214 highskill husband white male 45
## 1236 highskill tenant white female 30
## 1244 highskill husband white male 59
## 1246 lowskill tenant white female 20
## 1250 lowskill depchild white female 40
## 1251 highskill husband white male 50
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## 1264 lowskill tenant white female 37
## 1269 clerical husband white male 37
## 1271 highskill single_occ black female 45
## 1276 clerical husband white male 40
## 1280 highskill husband white male 40
## 1281 highskill tenant white male 50
## 1282 highskill single_occ Asian_Pac_Islander female 40
## 1285 clerical wife white female 40
## 1288 lowskill husband white male 40
## 1292 lowskill single_occ white female 30
## 1293 midskill husband white male 65
## 1295 lowskill husband white male 40
## 1302 lowskill single_occ black female 40
## 1306 midskill husband white male 43
## 1307 midskill tenant white male 40
## 1311 highskill husband white male 40
## 1316 midskill depchild white female 40
## 1320 midskill husband white male 45
## 1324 midskill husband white male 24
## 1335 highskill husband white male 40
## 1341 highskill single_occ white male 40
## 1344 clerical depchild white male 40
## 1350 other husband white male 50
## 1353 highskill depchild white female 40
## 1356 agriculture husband white male 60
## 1359 highskill tenant white male 50
## 1361 midskill tenant white male 41
## 1363 lowskill tenant white female 24
## 1366 highskill single_occ white female 40
## 1367 midskill wife black female 40
## 1369 highskill husband Asian_Pac_Islander male 40
## 1370 lowskill depchild white male 40
## 1376 highskill single_occ black female 40
## 1381 clerical depchild white male 40
## 1386 midskill depchild white male 45
## 1392 midskill husband black male 40
## 1396 midskill single_occ white male 40
## 1397 midskill husband black male 40
## 1401 clerical tenant white female 40
## 1408 midskill wife white female 50
## 1409 midskill tenant white male 50
## 1410 midskill husband white male 40
## 1415 highskill husband white male 60
## 1421 other depchild white male 50
## 1428 lowskill wife black female 34
## 1430 midskill husband white male 40
## 1433 highskill tenant white male 50
## 1435 highskill tenant black male 40
## 1443 highskill husband white male 50
## 1444 midskill tenant white male 40
## 1449 highskill husband white male 45
## 1457 midskill tenant white male 45
## 1459 other tenant black female 2
## 1467 midskill husband white male 45
## 1471 midskill husband white male 5
## 1472 highskill depchild Asian_Pac_Islander male 40
## 1491 highskill husband white male 48
## 1505 other husband white male 40
## 1506 midskill husband white male 42
## 1507 highskill husband white male 40
## 1509 clerical tenant black female 14
## 1514 highskill husband white male 50
## 1516 lowskill tenant Asian_Pac_Islander male 40
## 1518 clerical wife white female 30
## 1520 lowskill depchild black male 9
## 1522 lowskill tenant white female 40
## 1537 highskill husband white male 40
## 1541 clerical depchild white female 30
## 1544 midskill husband white male 40
## 1545 other tenant white male 40
## 1548 clerical husband white male 40
## 1549 midskill husband white male 50
## 1562 midskill husband white male 40
## 1563 other tenant white female 16
## 1565 lowskill relative black female 40
## 1574 lowskill depchild white male 40
## 1577 agriculture tenant white male 52
## 1579 highskill tenant white male 50
## 1584 clerical tenant white male 40
## 1587 highskill single_occ white male 45
## 1592 highskill husband white male 40
## 1597 clerical wife white female 60
## 1605 highskill husband white male 40
## 1608 midskill depchild white male 40
## 1615 clerical depchild white male 30
## 1616 highskill husband white male 40
## 1620 midskill husband white male 40
## 1623 lowskill depchild Asian_Pac_Islander female 40
## 1624 highskill husband white male 40
## 1631 lowskill husband white male 70
## 1632 lowskill tenant white female 24
## 1636 highskill husband white male 50
## 1640 midskill husband white male 50
## 1641 lowskill relative black female 38
## 1647 midskill husband black male 40
## 1648 highskill husband white male 40
## 1652 midskill husband white male 30
## 1656 highskill husband white male 50
## 1663 highskill tenant white male 40
## 1665 lowskill husband black male 40
## 1673 midskill husband white male 40
## 1678 midskill tenant white male 40
## 1681 lowskill single_occ white female 12
## 1687 lowskill tenant black female 45
## 1688 highskill husband white male 50
## 1689 lowskill tenant white female 36
## 1692 clerical depchild white female 20
## 1721 clerical tenant white female 40
## 1724 highskill husband white male 42
## 1726 midskill tenant white male 40
## 1729 highskill wife white female 50
## 1734 lowskill tenant white male 38
## 1755 highskill husband black male 45
## 1767 lowskill relative black male 40
## 1768 midskill husband white male 40
## 1780 highskill husband white male 40
## 1784 highskill husband white male 50
## 1792 midskill tenant white male 40
## 1796 highskill husband white male 40
## 1800 highskill tenant white female 40
## 1801 clerical husband white male 40
## 1805 highskill tenant white male 40
## 1816 lowskill depchild black male 40
## 1817 midskill husband white male 40
## 1825 midskill husband white male 84
## 1830 midskill single_occ white female 55
## 1831 lowskill depchild white male 10
## 1837 highskill husband white male 40
## 1841 midskill tenant white male 44
## 1847 other depchild white female 40
## 1848 lowskill depchild white female 40
## 1851 lowskill husband white male 40
## 1852 other husband white male 30
## 1855 midskill single_occ black male 45
## 1862 midskill husband white male 40
## 1865 highskill husband Asian_Pac_Islander male 54
## 1868 clerical tenant white female 48
## 1885 highskill single_occ white male 40
## 1889 lowskill depchild white male 40
## 1897 highskill husband white male 55
## 1898 midskill tenant white male 40
## 1899 midskill husband white male 45
## 1900 highskill depchild white male 50
## 1904 clerical tenant white female 40
## 1908 highskill tenant white female 50
## 1909 midskill husband white male 35
## 1918 highskill husband black male 46
## 1926 highskill tenant white male 55
## 1933 midskill relative Asian_Pac_Islander female 48
## 1934 highskill relative white female 48
## 1943 highskill single_occ black female 40
## 1950 highskill husband white male 40
## 1952 highskill husband white male 6
## 1959 highskill tenant white female 50
## 1964 highskill husband white male 50
## 1965 midskill single_occ white male 20
## 1980 lowskill depchild black male 40
## 1983 highskill tenant black female 40
## 1986 midskill tenant white male 48
## 1988 other wife white female 3
## 1990 highskill tenant black female 35
## 1993 highskill tenant white male 70
## 1997 highskill husband white male 60
## 2001 midskill single_occ white male 35
## 2007 clerical relative black female 40
## 2020 midskill depchild white male 40
## 2028 lowskill tenant white male 40
## 2042 lowskill single_occ black female 38
## 2050 highskill depchild white female 40
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## 13232 highskill tenant white male 50
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## 15492 lowskill tenant white female 40
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## 15531 highskill tenant white male 40
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## 32161 clerical depchild white female 20
## 32166 highskill tenant white female 12
## 32167 highskill single_occ black female 30
## 32174 highskill husband white male 60
## 32180 midskill husband white male 40
## 32181 highskill depchild white female 30
## 32189 highskill tenant white female 55
## 32193 clerical tenant white female 65
## 32215 lowskill relative white male 40
## 32216 highskill tenant white male 40
## 32217 lowskill husband white male 40
## 32218 highskill tenant white female 37
## 32225 lowskill tenant white female 20
## 32226 midskill husband white male 45
## 32230 highskill single_occ black female 40
## 32234 lowskill single_occ white female 40
## 32239 highskill husband white male 40
## 32240 clerical tenant white female 38
## 32243 midskill tenant native male 35
## 32247 agriculture husband white male 50
## 32249 highskill tenant white female 40
## 32251 highskill husband white male 50
## 32255 lowskill husband Asian_Pac_Islander male 55
## 32259 highskill husband white male 60
## 32262 highskill depchild white male 40
## 32266 lowskill husband white male 40
## 32287 highskill depchild black female 40
## 32288 highskill husband white male 60
## 32292 other tenant black female 40
## 32308 highskill husband white male 60
## 32310 midskill husband white male 40
## 32318 other tenant white female 5
## 32320 highskill tenant white female 37
## 32321 midskill husband white male 72
## 32326 lowskill depchild white female 37
## 32333 midskill husband white male 72
## 32335 agriculture tenant white male 40
## 32338 highskill tenant Asian_Pac_Islander male 55
## 32341 highskill tenant white male 45
## 32343 midskill husband black male 40
## 32352 midskill tenant white male 40
## 32357 clerical depchild white male 15
## 32365 midskill husband white male 76
## 32374 highskill tenant white female 40
## 32387 highskill wife white female 40
## 32396 midskill tenant white male 50
## 32399 lowskill relative white male 30
## 32413 lowskill tenant black male 40
## 32421 highskill tenant white female 50
## 32426 midskill wife native female 35
## 32428 lowskill single_occ white female 32
## 32431 agriculture husband white male 56
## 32435 lowskill single_occ white female 35
## 32436 midskill husband white male 40
## 32438 midskill tenant white male 49
## 32440 highskill depchild white male 40
## 32443 lowskill tenant black male 40
## 32456 highskill husband white male 50
## 32461 clerical tenant white female 32
## 32472 lowskill tenant white male 50
## 32475 midskill husband white male 52
## 32478 other relative black male 40
## 32479 lowskill depchild white male 40
## 32485 highskill tenant white female 40
## 32491 other tenant black male 35
## 32502 lowskill husband white male 40
## 32508 highskill husband white male 40
## 32513 midskill depchild white male 40
## 32518 midskill husband white male 40
## 32520 highskill husband white male 48
## 32522 midskill husband black male 40
## 32525 midskill relative white female 15
## 32526 other single_occ white female 1
## 32535 clerical single_occ white female 39
## 32550 clerical relative white female 40
## 32558 midskill husband white male 40
## native_region income capital_gain_log capital_loss_log
## 4 NorthAmerica <=50K 0.000000 0.000000
## 5 CentrAmerica <=50K 0.000000 0.000000
## 8 NorthAmerica >50K 0.000000 0.000000
## 9 NorthAmerica >50K 9.552866 0.000000
## 11 NorthAmerica >50K 0.000000 0.000000
## 12 SAsia >50K 0.000000 0.000000
## 17 NorthAmerica <=50K 0.000000 0.000000
## 19 NorthAmerica <=50K 0.000000 0.000000
## 21 NorthAmerica >50K 0.000000 0.000000
## 27 NorthAmerica <=50K 0.000000 0.000000
## 33 NorthAmerica <=50K 0.000000 7.250636
## 37 NorthAmerica <=50K 0.000000 0.000000
## 39 CentrAmerica >50K 0.000000 0.000000
## 46 NorthAmerica >50K 0.000000 0.000000
## 47 NorthAmerica <=50K 0.000000 0.000000
## 52 CentrAmerica <=50K 0.000000 0.000000
## 55 NorthAmerica <=50K 0.000000 0.000000
## 56 NorthAmerica >50K 0.000000 0.000000
## 62 CentrAmerica <=50K 0.000000 0.000000
## 66 NorthAmerica <=50K 0.000000 0.000000
## 70 NorthAmerica <=50K 0.000000 0.000000
## 71 NorthAmerica <=50K 0.000000 0.000000
## 72 NorthAmerica <=50K 0.000000 0.000000
## 78 NorthAmerica <=50K 0.000000 0.000000
## 89 NorthAmerica <=50K 0.000000 0.000000
## 90 NorthAmerica >50K 0.000000 0.000000
## 93 NorthAmerica <=50K 0.000000 0.000000
## 96 NorthAmerica <=50K 0.000000 0.000000
## 98 NorthAmerica >50K 0.000000 0.000000
## 99 Europe <=50K 0.000000 0.000000
## 104 NorthAmerica <=50K 0.000000 0.000000
## 112 NorthAmerica >50K 0.000000 0.000000
## 114 NorthAmerica <=50K 8.310169 0.000000
## 116 NorthAmerica <=50K 0.000000 0.000000
## 117 NorthAmerica <=50K 0.000000 0.000000
## 128 NorthAmerica >50K 0.000000 0.000000
## 132 NorthAmerica <=50K 0.000000 7.474772
## 136 SAsia >50K 0.000000 0.000000
## 142 NorthAmerica <=50K 0.000000 0.000000
## 143 NorthAmerica <=50K 0.000000 0.000000
## 151 NorthAmerica <=50K 0.000000 0.000000
## 154 NorthAmerica <=50K 0.000000 0.000000
## 155 NorthAmerica >50K 0.000000 0.000000
## 166 NorthAmerica <=50K 0.000000 0.000000
## 168 NorthAmerica <=50K 0.000000 0.000000
## 178 NorthAmerica <=50K 0.000000 0.000000
## 190 NorthAmerica >50K 0.000000 0.000000
## 197 NorthAmerica <=50K 0.000000 0.000000
## 201 NorthAmerica <=50K 0.000000 7.537430
## 205 NorthAmerica <=50K 0.000000 0.000000
## 212 NorthAmerica >50K 0.000000 0.000000
## 213 NorthAmerica <=50K 0.000000 0.000000
## 223 NorthAmerica <=50K 0.000000 7.699389
## 226 NorthAmerica <=50K 0.000000 0.000000
## 240 NorthAmerica <=50K 0.000000 0.000000
## 243 NorthAmerica <=50K 0.000000 0.000000
## 245 NorthAmerica <=50K 0.000000 7.462789
## 246 CentrAmerica <=50K 0.000000 0.000000
## 247 NorthAmerica >50K 0.000000 0.000000
## 255 NorthAmerica <=50K 0.000000 0.000000
## 257 NorthAmerica <=50K 0.000000 0.000000
## 266 EAsia >50K 0.000000 0.000000
## 268 NorthAmerica >50K 0.000000 0.000000
## 276 NorthAmerica <=50K 0.000000 0.000000
## 281 Europe <=50K 0.000000 0.000000
## 283 NorthAmerica <=50K 0.000000 0.000000
## 288 NorthAmerica <=50K 0.000000 0.000000
## 295 NorthAmerica <=50K 0.000000 0.000000
## 297 NorthAmerica <=50K 0.000000 0.000000
## 299 NorthAmerica <=50K 0.000000 0.000000
## 306 NorthAmerica <=50K 0.000000 0.000000
## 309 NorthAmerica >50K 0.000000 0.000000
## 311 NorthAmerica <=50K 0.000000 0.000000
## 314 NorthAmerica >50K 0.000000 0.000000
## 323 NorthAmerica <=50K 0.000000 0.000000
## 326 NorthAmerica <=50K 0.000000 0.000000
## 333 NorthAmerica <=50K 0.000000 0.000000
## 335 NorthAmerica <=50K 0.000000 0.000000
## 342 NorthAmerica >50K 8.895493 0.000000
## 343 NorthAmerica >50K 8.895493 0.000000
## 354 NorthAmerica <=50K 0.000000 0.000000
## 355 NorthAmerica >50K 0.000000 0.000000
## 356 NorthAmerica >50K 0.000000 0.000000
## 357 NorthAmerica <=50K 0.000000 0.000000
## 360 Europe <=50K 0.000000 0.000000
## 371 NorthAmerica <=50K 0.000000 0.000000
## 373 NorthAmerica >50K 0.000000 0.000000
## 375 NorthAmerica <=50K 0.000000 0.000000
## 381 NorthAmerica <=50K 0.000000 0.000000
## 384 NorthAmerica <=50K 0.000000 0.000000
## 388 NorthAmerica >50K 0.000000 7.789869
## 392 NorthAmerica <=50K 0.000000 0.000000
## 395 NorthAmerica <=50K 0.000000 0.000000
## 404 NorthAmerica <=50K 0.000000 0.000000
## 408 NorthAmerica <=50K 0.000000 0.000000
## 409 NorthAmerica >50K 0.000000 7.543273
## 410 NorthAmerica <=50K 0.000000 0.000000
## 418 NorthAmerica <=50K 0.000000 0.000000
## 422 NorthAmerica <=50K 7.685703 0.000000
## 423 NorthAmerica >50K 0.000000 0.000000
## 432 NorthAmerica <=50K 0.000000 0.000000
## 444 NorthAmerica >50K 0.000000 0.000000
## 453 NorthAmerica >50K 0.000000 0.000000
## 455 NorthAmerica >50K 0.000000 0.000000
## 456 NorthAmerica >50K 0.000000 0.000000
## 462 NorthAmerica <=50K 0.000000 0.000000
## 468 NorthAmerica <=50K 0.000000 0.000000
## 470 NorthAmerica >50K 0.000000 0.000000
## 477 NorthAmerica <=50K 0.000000 0.000000
## 478 NorthAmerica <=50K 0.000000 0.000000
## 480 NorthAmerica <=50K 0.000000 0.000000
## 496 NorthAmerica <=50K 0.000000 0.000000
## 498 CentrAmerica <=50K 0.000000 0.000000
## 502 NorthAmerica <=50K 0.000000 0.000000
## 504 Europe <=50K 0.000000 0.000000
## 508 NorthAmerica <=50K 0.000000 0.000000
## 511 NorthAmerica <=50K 0.000000 7.230563
## 512 NorthAmerica <=50K 0.000000 0.000000
## 524 NorthAmerica >50K 0.000000 0.000000
## 526 NorthAmerica <=50K 0.000000 0.000000
## 527 NorthAmerica >50K 0.000000 0.000000
## 530 NorthAmerica <=50K 0.000000 0.000000
## 533 NorthAmerica <=50K 0.000000 0.000000
## 534 NorthAmerica >50K 0.000000 0.000000
## 535 NorthAmerica >50K 9.906084 0.000000
## 536 NorthAmerica <=50K 0.000000 0.000000
## 538 NorthAmerica <=50K 0.000000 0.000000
## 542 NorthAmerica <=50K 0.000000 0.000000
## 544 NorthAmerica <=50K 0.000000 0.000000
## 547 NorthAmerica <=50K 0.000000 0.000000
## 552 NorthAmerica <=50K 0.000000 0.000000
## 553 NorthAmerica <=50K 0.000000 0.000000
## 560 NorthAmerica <=50K 0.000000 0.000000
## 565 NorthAmerica >50K 0.000000 0.000000
## 576 NorthAmerica >50K 0.000000 0.000000
## 579 NorthAmerica <=50K 0.000000 0.000000
## 580 NorthAmerica <=50K 0.000000 0.000000
## 583 NorthAmerica <=50K 0.000000 0.000000
## 584 NorthAmerica <=50K 0.000000 0.000000
## 587 NorthAmerica <=50K 0.000000 0.000000
## 590 NorthAmerica <=50K 0.000000 0.000000
## 594 NorthAmerica >50K 8.947546 0.000000
## 598 NorthAmerica >50K 0.000000 7.551187
## 603 NorthAmerica <=50K 0.000000 0.000000
## 632 NorthAmerica <=50K 0.000000 0.000000
## 637 NorthAmerica <=50K 0.000000 0.000000
## 638 NorthAmerica >50K 9.617471 0.000000
## 641 NorthAmerica <=50K 0.000000 0.000000
## 643 NorthAmerica <=50K 0.000000 0.000000
## 652 NorthAmerica <=50K 8.319230 0.000000
## 653 EAsia >50K 0.000000 0.000000
## 654 NorthAmerica <=50K 0.000000 0.000000
## 660 NorthAmerica >50K 0.000000 0.000000
## 670 NorthAmerica <=50K 0.000000 7.451242
## 672 NorthAmerica <=50K 0.000000 0.000000
## 681 NorthAmerica <=50K 0.000000 0.000000
## 683 NorthAmerica >50K 0.000000 0.000000
## 688 NorthAmerica <=50K 0.000000 0.000000
## 701 NorthAmerica <=50K 0.000000 0.000000
## 711 NorthAmerica <=50K 0.000000 0.000000
## 723 NorthAmerica <=50K 8.444838 0.000000
## 724 NorthAmerica >50K 0.000000 0.000000
## 727 NorthAmerica <=50K 0.000000 0.000000
## 728 NorthAmerica <=50K 0.000000 0.000000
## 730 CentrAmerica >50K 9.147081 0.000000
## 736 NorthAmerica <=50K 7.786552 0.000000
## 747 NorthAmerica >50K 8.947546 0.000000
## 748 NorthAmerica <=50K 0.000000 0.000000
## 755 NorthAmerica <=50K 0.000000 0.000000
## 764 NorthAmerica >50K 0.000000 0.000000
## 774 NorthAmerica >50K 0.000000 0.000000
## 781 CentrAmerica <=50K 0.000000 0.000000
## 785 NorthAmerica >50K 0.000000 0.000000
## 787 NorthAmerica <=50K 0.000000 7.763446
## 788 NorthAmerica <=50K 0.000000 0.000000
## 789 NorthAmerica <=50K 0.000000 0.000000
## 800 Europe >50K 0.000000 7.551187
## 802 NorthAmerica <=50K 0.000000 7.451242
## 813 NorthAmerica <=50K 0.000000 0.000000
## 818 NorthAmerica >50K 0.000000 0.000000
## 826 NorthAmerica >50K 0.000000 6.483107
## 830 NorthAmerica <=50K 0.000000 0.000000
## 832 NorthAmerica >50K 8.386401 0.000000
## 834 NorthAmerica <=50K 0.000000 0.000000
## 835 NorthAmerica <=50K 0.000000 0.000000
## 839 NorthAmerica <=50K 0.000000 0.000000
## 842 NorthAmerica <=50K 0.000000 0.000000
## 851 NorthAmerica >50K 0.000000 0.000000
## 852 NorthAmerica <=50K 0.000000 0.000000
## 853 NorthAmerica <=50K 0.000000 0.000000
## 855 NorthAmerica >50K 0.000000 0.000000
## 857 NorthAmerica <=50K 0.000000 0.000000
## 860 NorthAmerica >50K 0.000000 0.000000
## 862 NorthAmerica <=50K 0.000000 0.000000
## 870 NorthAmerica <=50K 0.000000 0.000000
## 871 NorthAmerica >50K 0.000000 0.000000
## 873 NorthAmerica >50K 9.269175 0.000000
## 881 NorthAmerica <=50K 0.000000 0.000000
## 889 NorthAmerica <=50K 0.000000 0.000000
## 894 NorthAmerica >50K 8.947546 0.000000
## 895 NorthAmerica >50K 0.000000 0.000000
## 897 NorthAmerica <=50K 0.000000 0.000000
## 899 NorthAmerica >50K 0.000000 0.000000
## 907 NorthAmerica <=50K 0.000000 0.000000
## 908 NorthAmerica <=50K 0.000000 0.000000
## 909 NorthAmerica <=50K 0.000000 0.000000
## 910 NorthAmerica <=50K 0.000000 0.000000
## 919 NorthAmerica <=50K 0.000000 0.000000
## 922 NorthAmerica <=50K 0.000000 0.000000
## 929 NorthAmerica <=50K 0.000000 0.000000
## 939 NorthAmerica <=50K 0.000000 0.000000
## 940 NorthAmerica >50K 0.000000 0.000000
## 942 NorthAmerica <=50K 0.000000 0.000000
## 944 NorthAmerica <=50K 0.000000 0.000000
## 952 NorthAmerica >50K 0.000000 0.000000
## 963 NorthAmerica <=50K 0.000000 0.000000
## 964 NorthAmerica <=50K 0.000000 0.000000
## 972 NorthAmerica <=50K 0.000000 0.000000
## 975 NorthAmerica <=50K 0.000000 0.000000
## 976 NorthAmerica <=50K 0.000000 0.000000
## 978 NorthAmerica >50K 0.000000 0.000000
## 1008 NorthAmerica >50K 0.000000 0.000000
## 1010 Europe <=50K 0.000000 0.000000
## 1011 NorthAmerica <=50K 0.000000 0.000000
## 1019 NorthAmerica <=50K 0.000000 0.000000
## 1027 CentrAmerica <=50K 0.000000 0.000000
## 1034 NorthAmerica <=50K 0.000000 0.000000
## 1038 NorthAmerica <=50K 0.000000 0.000000
## 1044 NorthAmerica >50K 8.947546 0.000000
## 1049 NorthAmerica <=50K 0.000000 0.000000
## 1053 NorthAmerica >50K 8.895493 0.000000
## 1056 NorthAmerica <=50K 0.000000 0.000000
## 1065 NorthAmerica >50K 0.000000 0.000000
## 1072 NorthAmerica >50K 0.000000 7.551187
## 1085 NorthAmerica <=50K 0.000000 0.000000
## 1087 NorthAmerica <=50K 0.000000 0.000000
## 1090 NorthAmerica <=50K 0.000000 0.000000
## 1099 NorthAmerica >50K 0.000000 7.589842
## 1102 NorthAmerica >50K 0.000000 0.000000
## 1107 NorthAmerica <=50K 0.000000 0.000000
## 1110 NorthAmerica <=50K 0.000000 0.000000
## 1114 NorthAmerica <=50K 0.000000 0.000000
## 1117 NorthAmerica <=50K 0.000000 0.000000
## 1118 NorthAmerica <=50K 0.000000 0.000000
## 1120 EAsia <=50K 0.000000 0.000000
## 1121 NorthAmerica <=50K 0.000000 0.000000
## 1122 NorthAmerica <=50K 8.490028 0.000000
## 1124 NorthAmerica <=50K 0.000000 0.000000
## 1129 NorthAmerica <=50K 0.000000 0.000000
## 1148 NorthAmerica <=50K 0.000000 0.000000
## 1149 NorthAmerica >50K 0.000000 0.000000
## 1151 NorthAmerica <=50K 0.000000 0.000000
## 1158 NorthAmerica >50K 0.000000 0.000000
## 1171 NorthAmerica <=50K 0.000000 0.000000
## 1173 NorthAmerica >50K 0.000000 0.000000
## 1180 NorthAmerica <=50K 0.000000 0.000000
## 1189 NorthAmerica <=50K 0.000000 0.000000
## 1204 NorthAmerica >50K 0.000000 0.000000
## 1211 NorthAmerica >50K 8.552367 0.000000
## 1214 NorthAmerica >50K 9.617471 0.000000
## 1236 NorthAmerica <=50K 0.000000 0.000000
## 1244 NorthAmerica >50K 0.000000 0.000000
## 1246 NorthAmerica <=50K 0.000000 0.000000
## 1250 NorthAmerica <=50K 0.000000 0.000000
## 1251 NorthAmerica >50K 9.617471 0.000000
## 1254 NorthAmerica <=50K 0.000000 0.000000
## 1264 NorthAmerica <=50K 0.000000 0.000000
## 1269 NorthAmerica <=50K 0.000000 0.000000
## 1271 NorthAmerica <=50K 0.000000 0.000000
## 1276 NorthAmerica >50K 0.000000 0.000000
## 1280 NorthAmerica <=50K 0.000000 0.000000
## 1281 NorthAmerica <=50K 0.000000 7.537430
## 1282 EAsia >50K 0.000000 0.000000
## 1285 NorthAmerica >50K 8.552367 0.000000
## 1288 NorthAmerica <=50K 0.000000 0.000000
## 1292 CentrAmerica <=50K 0.000000 0.000000
## 1293 NorthAmerica <=50K 0.000000 0.000000
## 1295 NorthAmerica <=50K 0.000000 0.000000
## 1302 EAsia <=50K 0.000000 0.000000
## 1306 NorthAmerica >50K 0.000000 0.000000
## 1307 NorthAmerica <=50K 0.000000 0.000000
## 1311 NorthAmerica <=50K 0.000000 0.000000
## 1316 NorthAmerica <=50K 0.000000 0.000000
## 1320 NorthAmerica >50K 0.000000 0.000000
## 1324 NorthAmerica >50K 0.000000 0.000000
## 1335 NorthAmerica >50K 0.000000 0.000000
## 1341 NorthAmerica <=50K 0.000000 0.000000
## 1344 NorthAmerica <=50K 0.000000 0.000000
## 1350 NorthAmerica <=50K 8.135054 0.000000
## 1353 NorthAmerica <=50K 0.000000 0.000000
## 1356 NorthAmerica >50K 0.000000 7.522400
## 1359 NorthAmerica <=50K 0.000000 0.000000
## 1361 NorthAmerica <=50K 0.000000 0.000000
## 1363 NorthAmerica <=50K 0.000000 0.000000
## 1366 NorthAmerica <=50K 0.000000 0.000000
## 1367 NorthAmerica <=50K 0.000000 0.000000
## 1369 EAsia >50K 11.512925 0.000000
## 1370 NorthAmerica <=50K 0.000000 0.000000
## 1376 NorthAmerica <=50K 0.000000 0.000000
## 1381 NorthAmerica <=50K 0.000000 0.000000
## 1386 NorthAmerica <=50K 0.000000 0.000000
## 1392 CentrAmerica >50K 0.000000 7.543273
## 1396 NorthAmerica >50K 0.000000 0.000000
## 1397 NorthAmerica <=50K 8.519989 0.000000
## 1401 NorthAmerica <=50K 0.000000 0.000000
## 1408 NorthAmerica >50K 0.000000 0.000000
## 1409 NorthAmerica <=50K 0.000000 0.000000
## 1410 NorthAmerica <=50K 0.000000 0.000000
## 1415 NorthAmerica <=50K 0.000000 0.000000
## 1421 NorthAmerica <=50K 0.000000 0.000000
## 1428 NorthAmerica <=50K 8.135054 0.000000
## 1430 NorthAmerica >50K 0.000000 7.543273
## 1433 NorthAmerica >50K 0.000000 0.000000
## 1435 NorthAmerica <=50K 0.000000 0.000000
## 1443 NorthAmerica >50K 0.000000 0.000000
## 1444 NorthAmerica <=50K 0.000000 0.000000
## 1449 NorthAmerica >50K 0.000000 0.000000
## 1457 NorthAmerica <=50K 8.109526 0.000000
## 1459 NorthAmerica <=50K 0.000000 0.000000
## 1467 NorthAmerica <=50K 0.000000 7.626570
## 1471 NorthAmerica >50K 0.000000 0.000000
## 1472 NorthAmerica <=50K 0.000000 0.000000
## 1491 NorthAmerica <=50K 0.000000 0.000000
## 1505 NorthAmerica <=50K 0.000000 0.000000
## 1506 NorthAmerica <=50K 0.000000 0.000000
## 1507 NorthAmerica <=50K 0.000000 0.000000
## 1509 NorthAmerica <=50K 0.000000 0.000000
## 1514 NorthAmerica <=50K 0.000000 0.000000
## 1516 EAsia <=50K 0.000000 0.000000
## 1518 NorthAmerica <=50K 7.967627 0.000000
## 1520 NorthAmerica <=50K 0.000000 0.000000
## 1522 NorthAmerica <=50K 0.000000 0.000000
## 1537 NorthAmerica <=50K 0.000000 0.000000
## 1541 NorthAmerica <=50K 0.000000 0.000000
## 1544 NorthAmerica <=50K 0.000000 0.000000
## 1545 NorthAmerica <=50K 0.000000 0.000000
## 1548 NorthAmerica <=50K 0.000000 0.000000
## 1549 NorthAmerica >50K 0.000000 7.543273
## 1562 NorthAmerica <=50K 0.000000 0.000000
## 1563 NorthAmerica >50K 10.131619 0.000000
## 1565 NorthAmerica <=50K 0.000000 0.000000
## 1574 NorthAmerica <=50K 0.000000 0.000000
## 1577 NorthAmerica <=50K 0.000000 0.000000
## 1579 NorthAmerica <=50K 0.000000 0.000000
## 1584 NorthAmerica >50K 9.261129 0.000000
## 1587 Europe <=50K 0.000000 0.000000
## 1592 NorthAmerica <=50K 0.000000 0.000000
## 1597 NorthAmerica <=50K 0.000000 0.000000
## 1605 NorthAmerica <=50K 0.000000 0.000000
## 1608 NorthAmerica <=50K 0.000000 0.000000
## 1615 NorthAmerica <=50K 0.000000 0.000000
## 1616 NorthAmerica >50K 0.000000 0.000000
## 1620 NorthAmerica <=50K 0.000000 0.000000
## 1623 EAsia <=50K 0.000000 0.000000
## 1624 NorthAmerica <=50K 0.000000 0.000000
## 1631 NorthAmerica <=50K 7.786552 0.000000
## 1632 NorthAmerica <=50K 0.000000 0.000000
## 1636 NorthAmerica >50K 0.000000 0.000000
## 1640 NorthAmerica >50K 8.552367 0.000000
## 1641 NorthAmerica <=50K 0.000000 0.000000
## 1647 NorthAmerica <=50K 0.000000 0.000000
## 1648 NorthAmerica <=50K 0.000000 0.000000
## 1652 NorthAmerica <=50K 0.000000 0.000000
## 1656 Europe >50K 9.617471 0.000000
## 1663 NorthAmerica <=50K 0.000000 0.000000
## 1665 NorthAmerica <=50K 0.000000 0.000000
## 1673 NorthAmerica <=50K 7.993620 0.000000
## 1678 NorthAmerica <=50K 0.000000 0.000000
## 1681 NorthAmerica <=50K 0.000000 0.000000
## 1687 NorthAmerica <=50K 0.000000 0.000000
## 1688 NorthAmerica >50K 0.000000 7.543273
## 1689 NorthAmerica <=50K 0.000000 0.000000
## 1692 NorthAmerica <=50K 6.962243 0.000000
## 1721 NorthAmerica <=50K 0.000000 0.000000
## 1724 NorthAmerica >50K 0.000000 0.000000
## 1726 NorthAmerica <=50K 0.000000 0.000000
## 1729 NorthAmerica >50K 0.000000 0.000000
## 1734 NorthAmerica <=50K 0.000000 0.000000
## 1755 NorthAmerica >50K 0.000000 0.000000
## 1767 NorthAmerica <=50K 0.000000 0.000000
## 1768 NorthAmerica <=50K 0.000000 0.000000
## 1780 NorthAmerica >50K 8.947546 0.000000
## 1784 NorthAmerica >50K 0.000000 0.000000
## 1792 NorthAmerica <=50K 0.000000 0.000000
## 1796 NorthAmerica >50K 8.040447 0.000000
## 1800 NorthAmerica <=50K 0.000000 0.000000
## 1801 NorthAmerica <=50K 0.000000 0.000000
## 1805 NorthAmerica <=50K 7.684784 0.000000
## 1816 NorthAmerica <=50K 0.000000 0.000000
## 1817 NorthAmerica <=50K 0.000000 7.551187
## 1825 NorthAmerica >50K 0.000000 0.000000
## 1830 NorthAmerica <=50K 0.000000 0.000000
## 1831 NorthAmerica <=50K 0.000000 0.000000
## 1837 NorthAmerica <=50K 0.000000 0.000000
## 1841 NorthAmerica <=50K 0.000000 0.000000
## 1847 NorthAmerica <=50K 0.000000 0.000000
## 1848 USTerritory <=50K 0.000000 0.000000
## 1851 NorthAmerica <=50K 0.000000 0.000000
## 1852 NorthAmerica <=50K 0.000000 0.000000
## 1855 NorthAmerica <=50K 0.000000 0.000000
## 1862 NorthAmerica <=50K 0.000000 0.000000
## 1865 NorthAmerica >50K 0.000000 0.000000
## 1868 NorthAmerica <=50K 0.000000 0.000000
## 1885 NorthAmerica <=50K 0.000000 0.000000
## 1889 Europe <=50K 0.000000 0.000000
## 1897 NorthAmerica <=50K 0.000000 0.000000
## 1898 NorthAmerica <=50K 0.000000 0.000000
## 1899 NorthAmerica <=50K 0.000000 0.000000
## 1900 NorthAmerica <=50K 0.000000 0.000000
## 1904 NorthAmerica <=50K 0.000000 0.000000
## 1908 NorthAmerica <=50K 0.000000 0.000000
## 1909 NorthAmerica <=50K 0.000000 0.000000
## 1918 NorthAmerica <=50K 0.000000 0.000000
## 1926 NorthAmerica >50K 0.000000 0.000000
## 1933 EAsia <=50K 0.000000 0.000000
## 1934 NorthAmerica >50K 0.000000 0.000000
## 1943 NorthAmerica <=50K 0.000000 0.000000
## 1950 NorthAmerica >50K 0.000000 0.000000
## 1952 NorthAmerica <=50K 0.000000 0.000000
## 1959 NorthAmerica <=50K 0.000000 0.000000
## 1964 NorthAmerica >50K 0.000000 0.000000
## 1965 NorthAmerica <=50K 0.000000 0.000000
## 1980 NorthAmerica <=50K 0.000000 0.000000
## 1983 NorthAmerica <=50K 0.000000 0.000000
## 1986 NorthAmerica <=50K 0.000000 0.000000
## 1988 NorthAmerica <=50K 0.000000 0.000000
## 1990 NorthAmerica <=50K 0.000000 0.000000
## 1993 NorthAmerica >50K 0.000000 0.000000
## 1997 NorthAmerica >50K 0.000000 0.000000
## 2001 NorthAmerica <=50K 0.000000 0.000000
## 2007 CentrAmerica <=50K 0.000000 0.000000
## 2020 NorthAmerica <=50K 0.000000 0.000000
## 2028 NorthAmerica <=50K 0.000000 0.000000
## 2042 NorthAmerica <=50K 0.000000 0.000000
## 2050 NorthAmerica <=50K 0.000000 0.000000
## 2053 NorthAmerica <=50K 0.000000 0.000000
## 2055 NorthAmerica <=50K 0.000000 0.000000
## 2057 NorthAmerica >50K 9.061260 0.000000
## 2059 NorthAmerica <=50K 0.000000 0.000000
## 2077 NorthAmerica <=50K 0.000000 0.000000
## 2085 CentrAmerica <=50K 0.000000 0.000000
## 2092 NorthAmerica <=50K 0.000000 0.000000
## 2097 EAsia <=50K 0.000000 0.000000
## 2109 NorthAmerica <=50K 0.000000 0.000000
## 2117 NorthAmerica <=50K 0.000000 7.420579
## 2118 NorthAmerica <=50K 0.000000 0.000000
## 2121 NorthAmerica <=50K 0.000000 0.000000
## 2128 NorthAmerica <=50K 0.000000 0.000000
## 2138 USTerritory <=50K 0.000000 0.000000
## 2155 NorthAmerica <=50K 0.000000 0.000000
## 2161 NorthAmerica >50K 8.895493 0.000000
## 2162 NorthAmerica <=50K 0.000000 0.000000
## 2184 NorthAmerica <=50K 0.000000 0.000000
## 2194 NorthAmerica <=50K 0.000000 0.000000
## 2196 NorthAmerica <=50K 0.000000 0.000000
## 2200 NorthAmerica <=50K 0.000000 0.000000
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## 3572 NorthAmerica <=50K 0.000000 0.000000
## 3573 NorthAmerica <=50K 6.962243 0.000000
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## 7203 EAsia >50K 0.000000 7.589842
## 7211 EAsia <=50K 7.652546 0.000000
## 7226 NorthAmerica <=50K 0.000000 0.000000
## 7230 NorthAmerica <=50K 0.000000 0.000000
## 7243 NorthAmerica <=50K 0.000000 0.000000
## 7245 NorthAmerica <=50K 0.000000 0.000000
## 7247 NorthAmerica <=50K 0.000000 0.000000
## 7251 NorthAmerica <=50K 0.000000 0.000000
## 7270 NorthAmerica >50K 0.000000 0.000000
## 7273 NorthAmerica >50K 9.617471 0.000000
## 7278 NorthAmerica <=50K 0.000000 0.000000
## 7282 NorthAmerica <=50K 0.000000 0.000000
## 7288 Europe >50K 0.000000 0.000000
## 7296 NorthAmerica <=50K 0.000000 0.000000
## 7302 NorthAmerica <=50K 0.000000 0.000000
## 7316 NorthAmerica <=50K 0.000000 7.379632
## 7318 NorthAmerica >50K 0.000000 0.000000
## 7321 EAsia <=50K 0.000000 0.000000
## 7325 NorthAmerica <=50K 0.000000 0.000000
## 7327 NorthAmerica <=50K 0.000000 0.000000
## 7332 NorthAmerica <=50K 0.000000 0.000000
## 7333 NorthAmerica <=50K 0.000000 0.000000
## 7334 NorthAmerica <=50K 0.000000 0.000000
## 7337 NorthAmerica >50K 0.000000 0.000000
## 7339 NorthAmerica <=50K 0.000000 0.000000
## 7358 NorthAmerica <=50K 0.000000 0.000000
## 7365 NorthAmerica <=50K 0.000000 0.000000
## 7366 NorthAmerica <=50K 0.000000 0.000000
## 7377 NorthAmerica >50K 0.000000 0.000000
## 7384 NorthAmerica >50K 0.000000 0.000000
## 7386 NorthAmerica >50K 10.233834 0.000000
## 7389 NorthAmerica <=50K 0.000000 0.000000
## 7393 NorthAmerica <=50K 0.000000 0.000000
## 7401 NorthAmerica <=50K 0.000000 0.000000
## 7410 NorthAmerica >50K 8.947546 0.000000
## 7412 NorthAmerica <=50K 0.000000 0.000000
## 7417 NorthAmerica >50K 9.617471 0.000000
## 7422 NorthAmerica <=50K 0.000000 0.000000
## 7425 NorthAmerica <=50K 0.000000 0.000000
## 7427 NorthAmerica <=50K 0.000000 0.000000
## 7435 NorthAmerica <=50K 0.000000 0.000000
## 7440 NorthAmerica <=50K 0.000000 0.000000
## 7446 NorthAmerica <=50K 0.000000 0.000000
## 7447 NorthAmerica <=50K 0.000000 0.000000
## 7455 NorthAmerica <=50K 0.000000 0.000000
## 7485 NorthAmerica <=50K 0.000000 0.000000
## 7487 NorthAmerica <=50K 0.000000 0.000000
## 7493 NorthAmerica >50K 0.000000 0.000000
## 7504 NorthAmerica >50K 0.000000 7.543273
## 7508 SouthAmerica <=50K 0.000000 0.000000
## 7512 NorthAmerica <=50K 0.000000 0.000000
## 7513 NorthAmerica <=50K 0.000000 0.000000
## 7515 NorthAmerica <=50K 0.000000 0.000000
## 7517 NorthAmerica <=50K 0.000000 0.000000
## 7529 NorthAmerica <=50K 0.000000 0.000000
## 7531 NorthAmerica <=50K 0.000000 0.000000
## 7537 NorthAmerica >50K 0.000000 0.000000
## 7539 NorthAmerica <=50K 0.000000 0.000000
## 7548 NorthAmerica <=50K 0.000000 0.000000
## 7549 NorthAmerica <=50K 0.000000 0.000000
## 7550 NorthAmerica <=50K 0.000000 0.000000
## 7553 NorthAmerica <=50K 0.000000 0.000000
## 7556 NorthAmerica >50K 0.000000 0.000000
## 7559 NorthAmerica >50K 0.000000 0.000000
## 7576 NorthAmerica <=50K 0.000000 0.000000
## 7579 NorthAmerica <=50K 0.000000 0.000000
## 7582 NorthAmerica <=50K 0.000000 0.000000
## 7606 NorthAmerica <=50K 0.000000 0.000000
## 7609 NorthAmerica <=50K 0.000000 0.000000
## 7617 NorthAmerica <=50K 7.877018 0.000000
## 7621 NorthAmerica <=50K 0.000000 0.000000
## 7625 NorthAmerica >50K 8.895493 0.000000
## 7632 NorthAmerica <=50K 0.000000 0.000000
## 7637 NorthAmerica <=50K 0.000000 0.000000
## 7639 NorthAmerica >50K 0.000000 7.522400
## 7646 NorthAmerica >50K 0.000000 0.000000
## 7650 NorthAmerica >50K 0.000000 0.000000
## 7652 NorthAmerica <=50K 0.000000 7.365180
## 7659 NorthAmerica <=50K 8.319230 0.000000
## 7673 NorthAmerica <=50K 0.000000 0.000000
## 7676 NorthAmerica <=50K 0.000000 0.000000
## 7678 NorthAmerica <=50K 0.000000 0.000000
## 7679 NorthAmerica >50K 0.000000 0.000000
## 7681 NorthAmerica <=50K 0.000000 0.000000
## 7685 NorthAmerica <=50K 8.150468 0.000000
## 7688 NorthAmerica <=50K 0.000000 7.537430
## 7692 NorthAmerica <=50K 0.000000 0.000000
## 7711 NorthAmerica <=50K 0.000000 0.000000
## 7712 NorthAmerica <=50K 0.000000 0.000000
## 7714 NorthAmerica <=50K 0.000000 0.000000
## 7717 NorthAmerica >50K 8.947546 0.000000
## 7723 NorthAmerica <=50K 0.000000 0.000000
## 7730 NorthAmerica <=50K 0.000000 0.000000
## 7732 NorthAmerica <=50K 0.000000 0.000000
## 7733 SouthAmerica >50K 0.000000 0.000000
## 7736 Europe <=50K 0.000000 0.000000
## 7742 NorthAmerica >50K 0.000000 0.000000
## 7752 NorthAmerica <=50K 0.000000 0.000000
## 7753 NorthAmerica <=50K 0.000000 0.000000
## 7755 NorthAmerica <=50K 0.000000 0.000000
## 7760 NorthAmerica <=50K 0.000000 0.000000
## 7761 NorthAmerica <=50K 0.000000 0.000000
## 7762 NorthAmerica <=50K 0.000000 0.000000
## 7763 NorthAmerica <=50K 0.000000 0.000000
## 7773 NorthAmerica <=50K 0.000000 0.000000
## 7778 NorthAmerica >50K 0.000000 0.000000
## 7780 NorthAmerica <=50K 0.000000 0.000000
## 7788 NorthAmerica <=50K 0.000000 0.000000
## 7798 NorthAmerica <=50K 0.000000 0.000000
## 7807 NorthAmerica >50K 0.000000 0.000000
## 7812 NorthAmerica >50K 0.000000 0.000000
## 7814 NorthAmerica <=50K 0.000000 7.303843
## 7816 NorthAmerica <=50K 0.000000 0.000000
## 7817 NorthAmerica <=50K 0.000000 0.000000
## 7819 NorthAmerica >50K 0.000000 0.000000
## 7826 NorthAmerica <=50K 0.000000 0.000000
## 7839 NorthAmerica <=50K 0.000000 0.000000
## 7853 NorthAmerica <=50K 0.000000 0.000000
## 7860 Europe <=50K 0.000000 0.000000
## 7865 NorthAmerica <=50K 8.271037 0.000000
## 7869 NorthAmerica >50K 0.000000 0.000000
## 7870 NorthAmerica <=50K 0.000000 0.000000
## 7877 NorthAmerica <=50K 0.000000 0.000000
## 7878 NorthAmerica <=50K 0.000000 0.000000
## 7882 NorthAmerica <=50K 0.000000 0.000000
## 7885 NorthAmerica <=50K 0.000000 0.000000
## 7900 NorthAmerica <=50K 0.000000 0.000000
## 7901 NorthAmerica <=50K 8.150468 0.000000
## 7904 NorthAmerica <=50K 0.000000 0.000000
## 7907 NorthAmerica >50K 8.895493 0.000000
## 7910 EAsia <=50K 0.000000 0.000000
## 7921 NorthAmerica <=50K 0.000000 0.000000
## 7924 NorthAmerica <=50K 0.000000 0.000000
## 7933 SAsia <=50K 0.000000 0.000000
## 7943 NorthAmerica <=50K 0.000000 0.000000
## 7950 NorthAmerica <=50K 0.000000 0.000000
## 7961 CentrAmerica <=50K 0.000000 0.000000
## 7964 NorthAmerica <=50K 0.000000 0.000000
## 7969 NorthAmerica >50K 8.947546 0.000000
## 7974 NorthAmerica <=50K 0.000000 0.000000
## 7991 CentrAmerica <=50K 0.000000 0.000000
## 8002 NorthAmerica <=50K 0.000000 0.000000
## 8028 NorthAmerica <=50K 0.000000 0.000000
## 8048 NorthAmerica >50K 0.000000 0.000000
## 8049 NorthAmerica >50K 0.000000 0.000000
## 8054 NorthAmerica <=50K 0.000000 0.000000
## 8057 NorthAmerica <=50K 0.000000 0.000000
## 8058 NorthAmerica <=50K 8.150468 0.000000
## 8071 NorthAmerica <=50K 0.000000 0.000000
## 8072 NorthAmerica <=50K 0.000000 0.000000
## 8073 NorthAmerica <=50K 0.000000 0.000000
## 8076 EAsia <=50K 0.000000 0.000000
## 8083 NorthAmerica <=50K 0.000000 0.000000
## 8084 NorthAmerica <=50K 0.000000 7.303843
## 8087 NorthAmerica <=50K 0.000000 0.000000
## 8091 NorthAmerica <=50K 0.000000 0.000000
## 8100 CentrAmerica <=50K 0.000000 0.000000
## 8102 NorthAmerica <=50K 0.000000 0.000000
## 8103 NorthAmerica >50K 0.000000 7.551187
## 8126 NorthAmerica >50K 0.000000 0.000000
## 8128 NorthAmerica <=50K 0.000000 0.000000
## 8132 NorthAmerica <=50K 0.000000 0.000000
## 8138 NorthAmerica <=50K 0.000000 0.000000
## 8140 NorthAmerica >50K 0.000000 0.000000
## 8143 NorthAmerica <=50K 0.000000 0.000000
## 8144 NorthAmerica <=50K 0.000000 0.000000
## 8149 NorthAmerica <=50K 0.000000 0.000000
## 8152 NorthAmerica >50K 0.000000 0.000000
## 8153 EAsia >50K 0.000000 0.000000
## 8160 NorthAmerica <=50K 0.000000 0.000000
## 8161 CentrAmerica >50K 8.386401 0.000000
## 8163 NorthAmerica <=50K 0.000000 0.000000
## 8170 NorthAmerica <=50K 0.000000 0.000000
## 8176 NorthAmerica <=50K 0.000000 0.000000
## 8190 NorthAmerica <=50K 0.000000 0.000000
## 8201 NorthAmerica <=50K 0.000000 0.000000
## 8209 NorthAmerica >50K 0.000000 0.000000
## 8214 NorthAmerica >50K 9.617471 0.000000
## 8218 NorthAmerica <=50K 0.000000 0.000000
## 8219 NorthAmerica <=50K 0.000000 7.699389
## 8220 NorthAmerica <=50K 0.000000 0.000000
## 8222 NorthAmerica <=50K 0.000000 0.000000
## 8230 NorthAmerica <=50K 0.000000 0.000000
## 8231 NorthAmerica <=50K 0.000000 0.000000
## 8235 NorthAmerica >50K 0.000000 0.000000
## 8238 EAsia >50K 0.000000 0.000000
## 8245 NorthAmerica <=50K 0.000000 0.000000
## 8247 NorthAmerica <=50K 0.000000 0.000000
## 8248 NorthAmerica <=50K 0.000000 0.000000
## 8259 NorthAmerica >50K 0.000000 0.000000
## 8260 NorthAmerica <=50K 0.000000 0.000000
## 8262 NorthAmerica <=50K 0.000000 0.000000
## 8263 NorthAmerica >50K 0.000000 0.000000
## 8265 NorthAmerica <=50K 0.000000 0.000000
## 8268 NorthAmerica >50K 0.000000 0.000000
## 8273 NorthAmerica <=50K 0.000000 0.000000
## 8277 NorthAmerica <=50K 0.000000 0.000000
## 8281 NorthAmerica <=50K 0.000000 0.000000
## 8283 CentrAmerica <=50K 0.000000 7.663877
## 8294 NorthAmerica <=50K 0.000000 0.000000
## 8295 NorthAmerica <=50K 0.000000 0.000000
## 8300 NorthAmerica <=50K 0.000000 0.000000
## 8303 NorthAmerica <=50K 0.000000 0.000000
## 8311 NorthAmerica <=50K 0.000000 0.000000
## 8315 NorthAmerica <=50K 8.265650 0.000000
## 8320 NorthAmerica <=50K 0.000000 0.000000
## 8321 NorthAmerica <=50K 0.000000 0.000000
## 8332 NorthAmerica <=50K 0.000000 0.000000
## 8338 NorthAmerica <=50K 0.000000 0.000000
## 8351 CentrAmerica <=50K 0.000000 0.000000
## 8353 NorthAmerica <=50K 0.000000 0.000000
## 8359 NorthAmerica <=50K 0.000000 0.000000
## 8364 NorthAmerica >50K 9.617471 0.000000
## 8376 NorthAmerica <=50K 0.000000 0.000000
## 8378 NorthAmerica >50K 0.000000 0.000000
## 8382 NorthAmerica <=50K 0.000000 0.000000
## 8394 NorthAmerica >50K 0.000000 0.000000
## 8395 NorthAmerica >50K 0.000000 0.000000
## 8402 NorthAmerica >50K 0.000000 0.000000
## 8403 NorthAmerica >50K 0.000000 7.733246
## 8404 NorthAmerica <=50K 0.000000 0.000000
## 8441 NorthAmerica <=50K 0.000000 0.000000
## 8444 NorthAmerica <=50K 0.000000 0.000000
## 8445 NorthAmerica <=50K 0.000000 0.000000
## 8449 NorthAmerica >50K 0.000000 0.000000
## 8451 NorthAmerica <=50K 0.000000 5.780744
## 8457 NorthAmerica <=50K 0.000000 0.000000
## 8461 NorthAmerica <=50K 0.000000 0.000000
## 8477 NorthAmerica >50K 11.512925 0.000000
## 8480 NorthAmerica >50K 9.617471 0.000000
## 8485 NorthAmerica <=50K 0.000000 0.000000
## 8487 NorthAmerica <=50K 0.000000 0.000000
## 8491 NorthAmerica >50K 8.947546 0.000000
## 8492 NorthAmerica <=50K 0.000000 0.000000
## 8500 NorthAmerica <=50K 0.000000 0.000000
## 8502 NorthAmerica <=50K 0.000000 0.000000
## 8503 NorthAmerica <=50K 0.000000 0.000000
## 8511 NorthAmerica >50K 0.000000 0.000000
## 8520 NorthAmerica <=50K 0.000000 0.000000
## 8521 NorthAmerica <=50K 0.000000 0.000000
## 8523 NorthAmerica <=50K 0.000000 0.000000
## 8526 NorthAmerica >50K 0.000000 0.000000
## 8532 Europe <=50K 0.000000 0.000000
## 8537 NorthAmerica <=50K 0.000000 0.000000
## 8538 NorthAmerica <=50K 0.000000 0.000000
## 8541 CentrAmerica >50K 0.000000 0.000000
## 8548 NorthAmerica <=50K 0.000000 0.000000
## 8550 NorthAmerica <=50K 0.000000 0.000000
## 8561 NorthAmerica <=50K 0.000000 0.000000
## 8564 NorthAmerica <=50K 0.000000 0.000000
## 8567 NorthAmerica >50K 0.000000 0.000000
## 8568 NorthAmerica <=50K 0.000000 0.000000
## 8570 NorthAmerica <=50K 0.000000 7.372118
## 8575 NorthAmerica <=50K 0.000000 0.000000
## 8577 NorthAmerica <=50K 0.000000 0.000000
## 8578 NorthAmerica >50K 0.000000 0.000000
## 8582 NorthAmerica >50K 9.061260 0.000000
## 8586 NorthAmerica <=50K 8.652074 0.000000
## 8587 NorthAmerica <=50K 0.000000 0.000000
## 8600 NorthAmerica <=50K 0.000000 0.000000
## 8616 NorthAmerica <=50K 0.000000 0.000000
## 8623 NorthAmerica <=50K 0.000000 0.000000
## 8625 NorthAmerica <=50K 0.000000 0.000000
## 8627 NorthAmerica <=50K 0.000000 0.000000
## 8628 NorthAmerica <=50K 0.000000 0.000000
## 8632 NorthAmerica <=50K 0.000000 0.000000
## 8638 NorthAmerica <=50K 0.000000 0.000000
## 8641 NorthAmerica <=50K 0.000000 0.000000
## 8649 NorthAmerica <=50K 0.000000 0.000000
## 8651 NorthAmerica <=50K 0.000000 0.000000
## 8662 NorthAmerica <=50K 0.000000 0.000000
## 8665 NorthAmerica <=50K 0.000000 0.000000
## 8675 NorthAmerica >50K 0.000000 0.000000
## 8677 NorthAmerica <=50K 0.000000 0.000000
## 8683 NorthAmerica >50K 0.000000 0.000000
## 8684 NorthAmerica <=50K 0.000000 0.000000
## 8689 EAsia >50K 0.000000 0.000000
## 8692 NorthAmerica >50K 0.000000 0.000000
## 8701 NorthAmerica <=50K 0.000000 0.000000
## 8703 NorthAmerica <=50K 0.000000 0.000000
## 8711 NorthAmerica >50K 11.512925 0.000000
## 8721 NorthAmerica <=50K 0.000000 0.000000
## 8724 NorthAmerica <=50K 0.000000 0.000000
## 8731 NorthAmerica <=50K 0.000000 0.000000
## 8732 NorthAmerica <=50K 0.000000 0.000000
## 8736 NorthAmerica <=50K 7.948032 0.000000
## 8737 NorthAmerica <=50K 0.000000 0.000000
## 8751 NorthAmerica <=50K 0.000000 0.000000
## 8754 NorthAmerica <=50K 0.000000 0.000000
## 8767 NorthAmerica <=50K 0.000000 0.000000
## 8769 NorthAmerica >50K 0.000000 0.000000
## 8770 NorthAmerica <=50K 0.000000 0.000000
## 8773 NorthAmerica <=50K 8.604471 0.000000
## 8781 NorthAmerica <=50K 0.000000 0.000000
## 8782 NorthAmerica >50K 9.617471 0.000000
## 8783 NorthAmerica <=50K 0.000000 0.000000
## 8785 NorthAmerica <=50K 0.000000 0.000000
## 8790 NorthAmerica <=50K 8.319230 0.000000
## 8791 NorthAmerica >50K 0.000000 0.000000
## 8794 NorthAmerica <=50K 0.000000 0.000000
## 8799 NorthAmerica <=50K 0.000000 7.451242
## 8801 NorthAmerica >50K 0.000000 0.000000
## 8814 NorthAmerica <=50K 0.000000 0.000000
## 8818 NorthAmerica <=50K 0.000000 0.000000
## 8819 NorthAmerica <=50K 0.000000 0.000000
## 8824 NorthAmerica <=50K 0.000000 0.000000
## 8826 NorthAmerica >50K 0.000000 0.000000
## 8836 NorthAmerica <=50K 0.000000 0.000000
## 8840 NorthAmerica <=50K 0.000000 0.000000
## 8848 NorthAmerica <=50K 0.000000 0.000000
## 8855 NorthAmerica >50K 9.617471 0.000000
## 8858 NorthAmerica >50K 0.000000 0.000000
## 8859 NorthAmerica <=50K 0.000000 0.000000
## 8861 NorthAmerica <=50K 0.000000 0.000000
## 8865 NorthAmerica >50K 0.000000 0.000000
## 8866 NorthAmerica <=50K 0.000000 0.000000
## 8868 NorthAmerica <=50K 0.000000 0.000000
## 8870 NorthAmerica >50K 0.000000 0.000000
## 8883 NorthAmerica <=50K 0.000000 0.000000
## 8884 NorthAmerica >50K 0.000000 7.589842
## 8885 NorthAmerica <=50K 0.000000 0.000000
## 8903 NorthAmerica <=50K 0.000000 0.000000
## 8916 CentrAmerica <=50K 0.000000 0.000000
## 8924 NorthAmerica <=50K 0.000000 0.000000
## 8926 NorthAmerica <=50K 0.000000 0.000000
## 8931 NorthAmerica <=50K 0.000000 0.000000
## 8940 NorthAmerica >50K 0.000000 0.000000
## 8945 NorthAmerica <=50K 0.000000 0.000000
## 8947 NorthAmerica <=50K 0.000000 0.000000
## 8950 NorthAmerica <=50K 0.000000 0.000000
## 8952 NorthAmerica <=50K 0.000000 0.000000
## 8953 NorthAmerica >50K 0.000000 0.000000
## 8956 NorthAmerica >50K 0.000000 0.000000
## 8958 NorthAmerica <=50K 0.000000 0.000000
## 8959 NorthAmerica <=50K 8.310169 0.000000
## 8966 NorthAmerica <=50K 0.000000 0.000000
## 8967 NorthAmerica >50K 8.040447 0.000000
## 8970 NorthAmerica <=50K 0.000000 0.000000
## 8971 NorthAmerica <=50K 0.000000 0.000000
## 8973 NorthAmerica >50K 0.000000 0.000000
## 8976 NorthAmerica >50K 0.000000 0.000000
## 8979 NorthAmerica <=50K 0.000000 0.000000
## 8980 NorthAmerica >50K 0.000000 0.000000
## 8987 NorthAmerica <=50K 0.000000 0.000000
## 8989 NorthAmerica >50K 0.000000 7.589842
## 8990 NorthAmerica >50K 0.000000 0.000000
## 8994 NorthAmerica <=50K 0.000000 0.000000
## 8997 NorthAmerica <=50K 0.000000 0.000000
## 9008 NorthAmerica <=50K 0.000000 0.000000
## 9015 NorthAmerica >50K 0.000000 0.000000
## 9023 NorthAmerica >50K 0.000000 0.000000
## 9046 NorthAmerica <=50K 0.000000 0.000000
## 9047 NorthAmerica <=50K 0.000000 0.000000
## 9049 NorthAmerica <=50K 0.000000 0.000000
## 9058 NorthAmerica <=50K 0.000000 0.000000
## 9062 NorthAmerica <=50K 0.000000 0.000000
## 9063 NorthAmerica >50K 8.947546 0.000000
## 9071 NorthAmerica >50K 0.000000 0.000000
## 9077 NorthAmerica <=50K 0.000000 0.000000
## 9079 NorthAmerica <=50K 0.000000 0.000000
## 9084 NorthAmerica <=50K 0.000000 0.000000
## 9087 NorthAmerica <=50K 0.000000 7.372118
## 9093 NorthAmerica <=50K 0.000000 0.000000
## 9112 NorthAmerica <=50K 0.000000 0.000000
## 9115 NorthAmerica <=50K 0.000000 0.000000
## 9124 NorthAmerica >50K 0.000000 0.000000
## 9125 NorthAmerica <=50K 0.000000 0.000000
## 9128 NorthAmerica <=50K 0.000000 0.000000
## 9130 NorthAmerica >50K 0.000000 7.303843
## 9131 NorthAmerica >50K 0.000000 0.000000
## 9133 NorthAmerica <=50K 0.000000 0.000000
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## 9161 NorthAmerica <=50K 0.000000 0.000000
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## 9182 NorthAmerica <=50K 0.000000 0.000000
## 9187 NorthAmerica <=50K 0.000000 0.000000
## 9191 NorthAmerica >50K 0.000000 0.000000
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## 9232 NorthAmerica <=50K 0.000000 0.000000
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## 9237 NorthAmerica <=50K 0.000000 0.000000
## 9241 NorthAmerica <=50K 0.000000 0.000000
## 9252 NorthAmerica <=50K 0.000000 0.000000
## 9253 CentrAmerica <=50K 0.000000 7.365180
## 9255 NorthAmerica >50K 0.000000 0.000000
## 9261 NorthAmerica <=50K 0.000000 0.000000
## 9269 NorthAmerica >50K 0.000000 0.000000
## 9271 NorthAmerica <=50K 0.000000 0.000000
## 9276 NorthAmerica <=50K 0.000000 7.379632
## 9284 NorthAmerica <=50K 0.000000 0.000000
## 9300 NorthAmerica <=50K 0.000000 0.000000
## 9302 NorthAmerica <=50K 0.000000 0.000000
## 9304 NorthAmerica <=50K 0.000000 0.000000
## 9305 NorthAmerica <=50K 0.000000 0.000000
## 9310 NorthAmerica <=50K 0.000000 7.774015
## 9314 NorthAmerica <=50K 0.000000 0.000000
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## 9356 NorthAmerica <=50K 0.000000 0.000000
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## 9358 NorthAmerica <=50K 0.000000 0.000000
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## 9364 NorthAmerica <=50K 0.000000 0.000000
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## 9375 NorthAmerica <=50K 9.265491 0.000000
## 9380 NorthAmerica <=50K 0.000000 0.000000
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## 9391 NorthAmerica <=50K 0.000000 0.000000
## 9393 NorthAmerica >50K 0.000000 0.000000
## 9398 NorthAmerica <=50K 0.000000 7.622175
## 9399 NorthAmerica <=50K 0.000000 0.000000
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## 9408 NorthAmerica <=50K 0.000000 7.591357
## 9409 NorthAmerica <=50K 0.000000 0.000000
## 9416 NorthAmerica <=50K 0.000000 0.000000
## 9421 NorthAmerica <=50K 0.000000 0.000000
## 9423 NorthAmerica >50K 0.000000 0.000000
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## 9432 NorthAmerica <=50K 0.000000 0.000000
## 9434 NorthAmerica <=50K 0.000000 0.000000
## 9439 NorthAmerica <=50K 0.000000 7.537430
## 9441 NorthAmerica <=50K 0.000000 0.000000
## 9444 NorthAmerica <=50K 0.000000 0.000000
## 9447 NorthAmerica <=50K 0.000000 0.000000
## 9448 NorthAmerica <=50K 0.000000 0.000000
## 9450 NorthAmerica >50K 0.000000 0.000000
## 9454 NorthAmerica >50K 8.947546 0.000000
## 9455 NorthAmerica >50K 0.000000 0.000000
## 9457 NorthAmerica <=50K 0.000000 0.000000
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## 9462 NorthAmerica <=50K 0.000000 0.000000
## 9466 NorthAmerica >50K 0.000000 0.000000
## 9471 EAsia <=50K 0.000000 0.000000
## 9472 NorthAmerica <=50K 0.000000 0.000000
## 9475 NorthAmerica <=50K 0.000000 7.230563
## 9476 Europe <=50K 0.000000 0.000000
## 9483 NorthAmerica <=50K 0.000000 0.000000
## 9486 NorthAmerica <=50K 0.000000 0.000000
## 9487 NorthAmerica <=50K 0.000000 0.000000
## 9488 NorthAmerica <=50K 0.000000 0.000000
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## 9501 NorthAmerica >50K 0.000000 0.000000
## 9502 NorthAmerica <=50K 0.000000 0.000000
## 9516 NorthAmerica <=50K 7.619233 0.000000
## 9523 NorthAmerica <=50K 0.000000 0.000000
## 9526 EAsia <=50K 0.000000 0.000000
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## 9543 NorthAmerica <=50K 0.000000 0.000000
## 9544 NorthAmerica <=50K 0.000000 0.000000
## 9546 NorthAmerica <=50K 0.000000 0.000000
## 9547 NorthAmerica <=50K 0.000000 0.000000
## 9552 CentrAmerica <=50K 8.265650 0.000000
## 9553 NorthAmerica <=50K 0.000000 0.000000
## 9556 NorthAmerica <=50K 0.000000 0.000000
## 9557 NorthAmerica <=50K 0.000000 0.000000
## 9560 CentrAmerica >50K 10.233834 0.000000
## 9565 NorthAmerica <=50K 0.000000 0.000000
## 9568 NorthAmerica >50K 0.000000 0.000000
## 9581 CentrAmerica <=50K 0.000000 0.000000
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## 9608 NorthAmerica <=50K 0.000000 0.000000
## 9613 NorthAmerica <=50K 0.000000 0.000000
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## 9624 NorthAmerica <=50K 7.697575 0.000000
## 9628 NorthAmerica <=50K 0.000000 0.000000
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## 9639 NorthAmerica <=50K 0.000000 0.000000
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## 9644 NorthAmerica <=50K 0.000000 0.000000
## 9653 NorthAmerica >50K 8.386401 0.000000
## 9654 NorthAmerica <=50K 0.000000 0.000000
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## 12455 NorthAmerica <=50K 7.764296 0.000000
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## 14880 NorthAmerica <=50K 0.000000 0.000000
## 14887 NorthAmerica <=50K 0.000000 0.000000
## 14893 NorthAmerica <=50K 0.000000 0.000000
## 14909 EAsia <=50K 0.000000 0.000000
## 14913 NorthAmerica <=50K 0.000000 0.000000
## 14915 NorthAmerica <=50K 0.000000 0.000000
## 14917 NorthAmerica >50K 8.895493 0.000000
## 14926 NorthAmerica >50K 0.000000 0.000000
## 14928 EAsia <=50K 0.000000 0.000000
## 14930 NorthAmerica >50K 0.000000 0.000000
## 14931 NorthAmerica <=50K 0.000000 0.000000
## 14935 NorthAmerica <=50K 0.000000 0.000000
## 14940 NorthAmerica <=50K 0.000000 0.000000
## 14956 NorthAmerica <=50K 0.000000 0.000000
## 14970 NorthAmerica <=50K 0.000000 0.000000
## 14975 CentrAmerica <=50K 0.000000 0.000000
## 14977 NorthAmerica <=50K 0.000000 0.000000
## 14979 NorthAmerica <=50K 0.000000 0.000000
## 14983 NorthAmerica <=50K 0.000000 0.000000
## 14987 NorthAmerica <=50K 0.000000 0.000000
## 14989 NorthAmerica <=50K 0.000000 7.420579
## 14992 NorthAmerica >50K 8.040447 0.000000
## 14995 NorthAmerica <=50K 0.000000 0.000000
## 14997 Europe <=50K 0.000000 0.000000
## 15039 NorthAmerica <=50K 0.000000 0.000000
## 15042 NorthAmerica <=50K 0.000000 0.000000
## 15043 NorthAmerica <=50K 0.000000 0.000000
## 15045 NorthAmerica <=50K 8.779250 0.000000
## 15049 NorthAmerica <=50K 0.000000 0.000000
## 15051 NorthAmerica <=50K 0.000000 0.000000
## 15054 NorthAmerica <=50K 0.000000 0.000000
## 15055 NorthAmerica <=50K 0.000000 0.000000
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## 15061 NorthAmerica <=50K 0.000000 0.000000
## 15064 NorthAmerica <=50K 0.000000 0.000000
## 15067 NorthAmerica <=50K 0.000000 0.000000
## 15070 NorthAmerica <=50K 0.000000 0.000000
## 15071 NorthAmerica >50K 0.000000 0.000000
## 15073 NorthAmerica <=50K 0.000000 0.000000
## 15079 NorthAmerica <=50K 0.000000 0.000000
## 15088 NorthAmerica >50K 0.000000 0.000000
## 15089 NorthAmerica <=50K 0.000000 0.000000
## 15090 NorthAmerica >50K 0.000000 0.000000
## 15097 NorthAmerica <=50K 0.000000 0.000000
## 15098 NorthAmerica <=50K 0.000000 0.000000
## 15110 NorthAmerica <=50K 0.000000 0.000000
## 15122 NorthAmerica >50K 8.895493 0.000000
## 15124 NorthAmerica <=50K 0.000000 0.000000
## 15126 NorthAmerica >50K 8.895493 0.000000
## 15133 NorthAmerica >50K 9.514216 0.000000
## 15135 NorthAmerica <=50K 0.000000 0.000000
## 15138 NorthAmerica <=50K 0.000000 0.000000
## 15142 NorthAmerica >50K 0.000000 0.000000
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## 15167 NorthAmerica <=50K 0.000000 0.000000
## 15171 NorthAmerica <=50K 0.000000 0.000000
## 15172 NorthAmerica <=50K 0.000000 0.000000
## 15187 NorthAmerica >50K 0.000000 0.000000
## 15196 NorthAmerica <=50K 0.000000 0.000000
## 15199 NorthAmerica <=50K 0.000000 0.000000
## 15205 Europe >50K 0.000000 0.000000
## 15229 USTerritory <=50K 0.000000 0.000000
## 15237 NorthAmerica <=50K 0.000000 0.000000
## 15239 NorthAmerica <=50K 0.000000 0.000000
## 15243 NorthAmerica >50K 0.000000 0.000000
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## 15262 NorthAmerica >50K 0.000000 0.000000
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## 15286 NorthAmerica <=50K 0.000000 0.000000
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## 15303 NorthAmerica <=50K 0.000000 0.000000
## 15310 NorthAmerica <=50K 0.000000 0.000000
## 15316 NorthAmerica >50K 8.947546 0.000000
## 15324 NorthAmerica <=50K 0.000000 0.000000
## 15331 NorthAmerica <=50K 0.000000 7.422374
## 15332 NorthAmerica >50K 9.261129 0.000000
## 15334 NorthAmerica <=50K 0.000000 0.000000
## 15342 NorthAmerica >50K 0.000000 0.000000
## 15344 NorthAmerica >50K 0.000000 0.000000
## 15348 NorthAmerica >50K 0.000000 0.000000
## 15349 NorthAmerica <=50K 0.000000 0.000000
## 15350 NorthAmerica >50K 8.947546 0.000000
## 15352 NorthAmerica <=50K 0.000000 0.000000
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## 15397 NorthAmerica <=50K 0.000000 0.000000
## 15401 NorthAmerica <=50K 7.513164 0.000000
## 15404 EAsia <=50K 0.000000 0.000000
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## 15413 NorthAmerica <=50K 0.000000 0.000000
## 15416 NorthAmerica >50K 0.000000 0.000000
## 15423 NorthAmerica <=50K 0.000000 0.000000
## 15435 NorthAmerica <=50K 8.137103 0.000000
## 15439 NorthAmerica <=50K 0.000000 0.000000
## 15444 EAsia <=50K 7.967627 0.000000
## 15449 NorthAmerica <=50K 0.000000 0.000000
## 15450 NorthAmerica <=50K 0.000000 0.000000
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## 15470 NorthAmerica <=50K 0.000000 0.000000
## 15471 NorthAmerica >50K 8.895493 0.000000
## 15473 NorthAmerica <=50K 0.000000 0.000000
## 15478 NorthAmerica >50K 0.000000 0.000000
## 15480 NorthAmerica <=50K 0.000000 0.000000
## 15482 NorthAmerica >50K 0.000000 7.551187
## 15491 NorthAmerica <=50K 0.000000 0.000000
## 15492 NorthAmerica <=50K 0.000000 0.000000
## 15496 NorthAmerica >50K 8.947546 0.000000
## 15503 NorthAmerica <=50K 0.000000 0.000000
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## 15532 NorthAmerica <=50K 0.000000 0.000000
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## 15547 NorthAmerica >50K 0.000000 0.000000
## 15558 NorthAmerica <=50K 0.000000 0.000000
## 15572 NorthAmerica <=50K 0.000000 0.000000
## 15581 SouthAmerica <=50K 0.000000 0.000000
## 15585 Europe <=50K 0.000000 0.000000
## 15586 NorthAmerica <=50K 0.000000 6.781058
## 15591 NorthAmerica >50K 0.000000 0.000000
## 15596 NorthAmerica >50K 0.000000 0.000000
## 15599 NorthAmerica <=50K 0.000000 0.000000
## 15610 CentrAmerica <=50K 0.000000 0.000000
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## 15654 NorthAmerica <=50K 0.000000 0.000000
## 15672 NorthAmerica <=50K 7.948032 0.000000
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## 15699 SAsia <=50K 0.000000 0.000000
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## 15719 NorthAmerica >50K 0.000000 0.000000
## 15721 NorthAmerica <=50K 0.000000 0.000000
## 15723 NorthAmerica <=50K 0.000000 0.000000
## 15732 NorthAmerica >50K 0.000000 7.543273
## 15739 NorthAmerica >50K 0.000000 0.000000
## 15746 NorthAmerica >50K 9.552866 0.000000
## 15749 NorthAmerica <=50K 0.000000 0.000000
## 15751 NorthAmerica <=50K 0.000000 0.000000
## 15757 NorthAmerica <=50K 7.684784 0.000000
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## 15784 NorthAmerica >50K 0.000000 0.000000
## 15785 NorthAmerica <=50K 0.000000 0.000000
## 15791 NorthAmerica <=50K 0.000000 0.000000
## 15793 CentrAmerica <=50K 0.000000 7.303843
## 15797 NorthAmerica <=50K 6.962243 0.000000
## 15802 NorthAmerica <=50K 0.000000 0.000000
## 15810 NorthAmerica >50K 0.000000 0.000000
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## 15828 NorthAmerica <=50K 0.000000 0.000000
## 15830 NorthAmerica >50K 0.000000 0.000000
## 15831 NorthAmerica >50K 0.000000 0.000000
## 15837 Europe <=50K 0.000000 0.000000
## 15851 NorthAmerica <=50K 0.000000 0.000000
## 15857 NorthAmerica <=50K 0.000000 7.602401
## 15860 NorthAmerica <=50K 0.000000 0.000000
## 15861 NorthAmerica >50K 8.767018 0.000000
## 15863 CentrAmerica <=50K 0.000000 0.000000
## 15872 NorthAmerica <=50K 0.000000 0.000000
## 15886 NorthAmerica <=50K 0.000000 0.000000
## 15893 Europe >50K 0.000000 0.000000
## 15896 NorthAmerica <=50K 0.000000 0.000000
## 15909 NorthAmerica <=50K 0.000000 0.000000
## 15926 NorthAmerica <=50K 0.000000 0.000000
## 15928 NorthAmerica <=50K 0.000000 0.000000
## 15932 CentrAmerica <=50K 0.000000 0.000000
## 15938 NorthAmerica <=50K 0.000000 0.000000
## 15955 NorthAmerica >50K 0.000000 0.000000
## 15958 NorthAmerica <=50K 0.000000 0.000000
## 15966 NorthAmerica <=50K 7.317876 0.000000
## 15970 NorthAmerica <=50K 0.000000 0.000000
## 15973 NorthAmerica <=50K 0.000000 0.000000
## 15978 NorthAmerica <=50K 0.000000 7.316548
## 15993 NorthAmerica <=50K 0.000000 0.000000
## 16002 NorthAmerica <=50K 0.000000 0.000000
## 16005 NorthAmerica >50K 9.061260 0.000000
## 16023 NorthAmerica <=50K 8.279697 0.000000
## 16024 NorthAmerica <=50K 0.000000 0.000000
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## 16042 NorthAmerica <=50K 0.000000 0.000000
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## 16078 NorthAmerica <=50K 0.000000 0.000000
## 16081 NorthAmerica <=50K 0.000000 0.000000
## 16103 NorthAmerica >50K 0.000000 0.000000
## 16111 NorthAmerica <=50K 0.000000 7.451242
## 16113 NorthAmerica <=50K 0.000000 0.000000
## 16115 NorthAmerica <=50K 0.000000 0.000000
## 16119 CentrAmerica <=50K 0.000000 0.000000
## 16120 NorthAmerica <=50K 0.000000 0.000000
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## 16130 NorthAmerica <=50K 0.000000 0.000000
## 16133 NorthAmerica <=50K 0.000000 0.000000
## 16136 NorthAmerica <=50K 0.000000 0.000000
## 16140 NorthAmerica >50K 0.000000 7.789869
## 16143 CentrAmerica <=50K 0.000000 0.000000
## 16144 NorthAmerica <=50K 0.000000 0.000000
## 16150 NorthAmerica <=50K 0.000000 0.000000
## 16154 NorthAmerica <=50K 0.000000 7.537430
## 16159 NorthAmerica <=50K 0.000000 0.000000
## 16160 NorthAmerica <=50K 0.000000 0.000000
## 16168 NorthAmerica >50K 8.386401 0.000000
## 16172 Europe >50K 0.000000 0.000000
## 16179 NorthAmerica <=50K 0.000000 0.000000
## 16200 NorthAmerica <=50K 0.000000 0.000000
## 16201 EAsia <=50K 0.000000 0.000000
## 16208 NorthAmerica <=50K 0.000000 0.000000
## 16217 NorthAmerica <=50K 0.000000 0.000000
## 16221 NorthAmerica >50K 8.895493 0.000000
## 16223 NorthAmerica <=50K 0.000000 0.000000
## 16232 CentrAmerica <=50K 0.000000 0.000000
## 16237 NorthAmerica <=50K 0.000000 0.000000
## 16241 NorthAmerica <=50K 0.000000 0.000000
## 16243 NorthAmerica <=50K 0.000000 0.000000
## 16248 NorthAmerica <=50K 0.000000 0.000000
## 16252 NorthAmerica <=50K 0.000000 0.000000
## 16262 NorthAmerica >50K 0.000000 0.000000
## 16270 NorthAmerica >50K 0.000000 0.000000
## 16274 NorthAmerica <=50K 0.000000 0.000000
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## 16285 NorthAmerica >50K 0.000000 0.000000
## 16293 SouthAmerica <=50K 0.000000 0.000000
## 16294 NorthAmerica <=50K 0.000000 0.000000
## 16295 NorthAmerica <=50K 7.967627 0.000000
## 16299 NorthAmerica <=50K 0.000000 0.000000
## 16307 NorthAmerica >50K 0.000000 7.543273
## 16314 NorthAmerica <=50K 8.832004 0.000000
## 16315 NorthAmerica <=50K 0.000000 0.000000
## 16317 NorthAmerica <=50K 0.000000 0.000000
## 16320 NorthAmerica <=50K 0.000000 0.000000
## 16321 NorthAmerica >50K 0.000000 7.543273
## 16328 NorthAmerica <=50K 0.000000 0.000000
## 16333 NorthAmerica <=50K 0.000000 0.000000
## 16338 NorthAmerica >50K 0.000000 0.000000
## 16339 NorthAmerica <=50K 0.000000 0.000000
## 16346 NorthAmerica >50K 0.000000 0.000000
## 16347 NorthAmerica <=50K 0.000000 0.000000
## 16348 NorthAmerica <=50K 0.000000 0.000000
## 16362 Europe <=50K 0.000000 0.000000
## 16370 NorthAmerica <=50K 0.000000 0.000000
## 16373 CentrAmerica >50K 9.147081 0.000000
## 16379 NorthAmerica <=50K 0.000000 0.000000
## 16388 NorthAmerica <=50K 0.000000 0.000000
## 16392 EAsia <=50K 0.000000 0.000000
## 16396 NorthAmerica <=50K 0.000000 0.000000
## 16409 NorthAmerica <=50K 0.000000 0.000000
## 16413 NorthAmerica <=50K 0.000000 0.000000
## 16416 NorthAmerica <=50K 0.000000 0.000000
## 16420 NorthAmerica <=50K 0.000000 0.000000
## 16421 NorthAmerica <=50K 0.000000 0.000000
## 16425 NorthAmerica <=50K 0.000000 0.000000
## 16427 NorthAmerica <=50K 0.000000 0.000000
## 16429 NorthAmerica >50K 0.000000 0.000000
## 16432 NorthAmerica <=50K 0.000000 0.000000
## 16433 NorthAmerica <=50K 0.000000 0.000000
## 16437 NorthAmerica <=50K 0.000000 0.000000
## 16439 NorthAmerica >50K 0.000000 0.000000
## 16442 NorthAmerica <=50K 0.000000 0.000000
## 16446 NorthAmerica <=50K 0.000000 0.000000
## 16450 NorthAmerica >50K 0.000000 0.000000
## 16456 NorthAmerica <=50K 0.000000 7.588324
## 16458 NorthAmerica >50K 8.895493 0.000000
## 16460 NorthAmerica <=50K 0.000000 0.000000
## 16471 NorthAmerica >50K 0.000000 0.000000
## 16472 NorthAmerica <=50K 0.000000 0.000000
## 16481 NorthAmerica <=50K 0.000000 0.000000
## 16496 NorthAmerica <=50K 0.000000 0.000000
## 16506 NorthAmerica <=50K 0.000000 7.551187
## 16512 NorthAmerica <=50K 0.000000 0.000000
## 16515 NorthAmerica <=50K 0.000000 0.000000
## 16519 NorthAmerica >50K 0.000000 0.000000
## 16523 NorthAmerica <=50K 0.000000 0.000000
## 16528 NorthAmerica >50K 0.000000 0.000000
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## 16568 NorthAmerica <=50K 0.000000 0.000000
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## 16572 NorthAmerica <=50K 0.000000 0.000000
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## 16580 NorthAmerica <=50K 0.000000 0.000000
## 16589 NorthAmerica >50K 9.906084 0.000000
## 16601 NorthAmerica <=50K 0.000000 0.000000
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## 16603 NorthAmerica <=50K 0.000000 0.000000
## 16605 NorthAmerica >50K 8.767018 0.000000
## 16615 NorthAmerica <=50K 0.000000 0.000000
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## 16625 NorthAmerica <=50K 0.000000 0.000000
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## 16635 NorthAmerica <=50K 0.000000 0.000000
## 16636 CentrAmerica >50K 0.000000 0.000000
## 16643 NorthAmerica <=50K 0.000000 0.000000
## 16648 CentrAmerica <=50K 0.000000 0.000000
## 16655 NorthAmerica <=50K 0.000000 0.000000
## 16661 NorthAmerica <=50K 0.000000 7.588324
## 16662 NorthAmerica <=50K 0.000000 0.000000
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## 16753 NorthAmerica >50K 8.947546 0.000000
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## 16799 NorthAmerica <=50K 0.000000 0.000000
## 16801 NorthAmerica <=50K 0.000000 0.000000
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## 16817 Europe <=50K 0.000000 0.000000
## 16820 NorthAmerica <=50K 0.000000 0.000000
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## 16842 NorthAmerica <=50K 0.000000 0.000000
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## 16850 NorthAmerica <=50K 0.000000 0.000000
## 16851 NorthAmerica <=50K 0.000000 0.000000
## 16857 NorthAmerica >50K 8.715716 0.000000
## 16858 NorthAmerica <=50K 0.000000 0.000000
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## 16958 CentrAmerica <=50K 0.000000 0.000000
## 16965 NorthAmerica >50K 0.000000 0.000000
## 16966 NorthAmerica >50K 0.000000 0.000000
## 16971 NorthAmerica >50K 0.000000 0.000000
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## 17286 NorthAmerica <=50K 0.000000 0.000000
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## 17342 NorthAmerica <=50K 6.962243 0.000000
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## 17379 CentrAmerica <=50K 7.685703 0.000000
## 17383 EAsia <=50K 0.000000 7.537430
## 17385 NorthAmerica <=50K 0.000000 0.000000
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## 17455 NorthAmerica >50K 8.947546 0.000000
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## 17484 NorthAmerica <=50K 0.000000 0.000000
## 17495 NorthAmerica <=50K 7.883823 0.000000
## 17499 NorthAmerica >50K 0.000000 0.000000
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## 19853 NorthAmerica <=50K 0.000000 7.687080
## 19863 NorthAmerica <=50K 0.000000 0.000000
## 19865 NorthAmerica <=50K 0.000000 0.000000
## 19867 NorthAmerica >50K 8.947546 0.000000
## 19868 NorthAmerica <=50K 0.000000 0.000000
## 19874 NorthAmerica <=50K 0.000000 0.000000
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## 19882 NorthAmerica <=50K 0.000000 0.000000
## 19885 NorthAmerica <=50K 0.000000 0.000000
## 19889 NorthAmerica <=50K 0.000000 0.000000
## 19892 NorthAmerica >50K 0.000000 0.000000
## 19896 NorthAmerica >50K 0.000000 0.000000
## 19897 NorthAmerica >50K 0.000000 7.543273
## 19901 NorthAmerica >50K 11.512925 0.000000
## 19910 NorthAmerica <=50K 0.000000 0.000000
## 19911 NorthAmerica <=50K 0.000000 0.000000
## 19922 NorthAmerica <=50K 0.000000 0.000000
## 19923 NorthAmerica <=50K 8.271037 0.000000
## 19924 NorthAmerica >50K 8.947546 0.000000
## 19926 NorthAmerica >50K 8.895493 0.000000
## 19927 SAsia <=50K 0.000000 0.000000
## 19928 NorthAmerica <=50K 0.000000 0.000000
## 19932 NorthAmerica >50K 0.000000 0.000000
## 19935 NorthAmerica <=50K 0.000000 0.000000
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## 19939 NorthAmerica >50K 0.000000 0.000000
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## 19951 NorthAmerica >50K 0.000000 0.000000
## 19952 NorthAmerica <=50K 0.000000 0.000000
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## 19959 NorthAmerica <=50K 0.000000 0.000000
## 19961 NorthAmerica <=50K 0.000000 0.000000
## 19962 NorthAmerica <=50K 0.000000 0.000000
## 19969 NorthAmerica >50K 0.000000 7.589842
## 19974 NorthAmerica <=50K 0.000000 0.000000
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## 19994 NorthAmerica <=50K 0.000000 0.000000
## 19995 NorthAmerica <=50K 0.000000 0.000000
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## 20003 NorthAmerica <=50K 0.000000 0.000000
## 20011 NorthAmerica >50K 0.000000 0.000000
## 20031 NorthAmerica <=50K 0.000000 0.000000
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## 20053 NorthAmerica >50K 0.000000 0.000000
## 20056 NorthAmerica >50K 11.512925 0.000000
## 20076 NorthAmerica <=50K 0.000000 0.000000
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## 20085 NorthAmerica <=50K 0.000000 0.000000
## 20086 NorthAmerica <=50K 8.135054 0.000000
## 20090 NorthAmerica >50K 0.000000 0.000000
## 20093 NorthAmerica <=50K 0.000000 0.000000
## 20094 NorthAmerica >50K 0.000000 7.543273
## 20095 NorthAmerica <=50K 0.000000 0.000000
## 20110 NorthAmerica <=50K 6.388561 0.000000
## 20115 CentrAmerica <=50K 0.000000 0.000000
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## 20121 NorthAmerica >50K 0.000000 0.000000
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## 20138 EAsia >50K 0.000000 0.000000
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## 20144 NorthAmerica <=50K 0.000000 0.000000
## 20145 NorthAmerica >50K 0.000000 0.000000
## 20154 NorthAmerica <=50K 0.000000 0.000000
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## 20176 Europe <=50K 0.000000 0.000000
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## 20196 NorthAmerica <=50K 0.000000 0.000000
## 20201 NorthAmerica >50K 8.947546 0.000000
## 20206 NorthAmerica >50K 9.617471 0.000000
## 20210 NorthAmerica <=50K 0.000000 0.000000
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## 20213 NorthAmerica >50K 0.000000 0.000000
## 20214 NorthAmerica <=50K 0.000000 7.365180
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## 20249 NorthAmerica >50K 8.895493 0.000000
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## 20366 NorthAmerica <=50K 0.000000 6.439350
## 20369 NorthAmerica >50K 8.895493 0.000000
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## 20424 EAsia >50K 9.617471 0.000000
## 20435 NorthAmerica <=50K 7.709308 0.000000
## 20437 NorthAmerica <=50K 0.000000 0.000000
## 20440 NorthAmerica <=50K 0.000000 7.451242
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## 20521 NorthAmerica <=50K 7.862497 0.000000
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## 20558 NorthAmerica >50K 8.895493 0.000000
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## 20614 NorthAmerica >50K 11.512925 0.000000
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## 20662 NorthAmerica >50K 9.617471 0.000000
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## 20726 NorthAmerica >50K 0.000000 7.589842
## 20727 NorthAmerica <=50K 0.000000 0.000000
## 20732 NorthAmerica >50K 9.261129 0.000000
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## 20737 NorthAmerica >50K 0.000000 7.551187
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## 20771 NorthAmerica <=50K 0.000000 7.422374
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## 20856 NorthAmerica <=50K 7.786552 0.000000
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## 21000 NorthAmerica <=50K 0.000000 0.000000
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## 21013 NorthAmerica <=50K 0.000000 0.000000
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## 21055 NorthAmerica <=50K 0.000000 0.000000
## 21066 Europe >50K 0.000000 0.000000
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## 21075 NorthAmerica <=50K 0.000000 0.000000
## 21077 SouthAmerica <=50K 0.000000 0.000000
## 21078 NorthAmerica >50K 8.947546 0.000000
## 21083 NorthAmerica <=50K 0.000000 0.000000
## 21085 NorthAmerica <=50K 0.000000 0.000000
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## 21101 NorthAmerica <=50K 0.000000 0.000000
## 21108 NorthAmerica <=50K 0.000000 7.722678
## 21118 NorthAmerica <=50K 0.000000 0.000000
## 21121 NorthAmerica <=50K 0.000000 0.000000
## 21127 CentrAmerica <=50K 0.000000 0.000000
## 21129 SAsia >50K 0.000000 0.000000
## 21136 NorthAmerica >50K 0.000000 0.000000
## 21137 NorthAmerica <=50K 0.000000 0.000000
## 21140 NorthAmerica <=50K 0.000000 0.000000
## 21141 NorthAmerica <=50K 0.000000 0.000000
## 21148 NorthAmerica >50K 0.000000 7.789869
## 21155 NorthAmerica <=50K 0.000000 0.000000
## 21157 NorthAmerica >50K 0.000000 0.000000
## 21159 EAsia <=50K 0.000000 0.000000
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## 21193 NorthAmerica <=50K 0.000000 0.000000
## 21198 NorthAmerica <=50K 0.000000 0.000000
## 21199 NorthAmerica >50K 9.906084 0.000000
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## 21251 NorthAmerica >50K 0.000000 0.000000
## 21262 NorthAmerica >50K 9.617471 0.000000
## 21265 CentrAmerica <=50K 0.000000 0.000000
## 21266 NorthAmerica <=50K 0.000000 0.000000
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## 21286 NorthAmerica <=50K 0.000000 0.000000
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## 21295 NorthAmerica <=50K 0.000000 0.000000
## 21297 NorthAmerica <=50K 0.000000 7.316548
## 21301 NorthAmerica <=50K 0.000000 0.000000
## 21315 NorthAmerica >50K 0.000000 0.000000
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## 21338 NorthAmerica <=50K 0.000000 0.000000
## 21352 NorthAmerica >50K 0.000000 7.589842
## 21361 NorthAmerica <=50K 0.000000 0.000000
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## 21405 NorthAmerica <=50K 0.000000 0.000000
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## 21473 NorthAmerica <=50K 0.000000 0.000000
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## 21490 NorthAmerica >50K 11.512925 0.000000
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## 21532 CentrAmerica <=50K 0.000000 7.462215
## 21539 NorthAmerica <=50K 7.948032 0.000000
## 21550 NorthAmerica <=50K 0.000000 0.000000
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## 21577 NorthAmerica <=50K 0.000000 0.000000
## 21583 NorthAmerica >50K 9.617471 0.000000
## 21588 Europe >50K 9.261129 0.000000
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## 21646 NorthAmerica <=50K 0.000000 0.000000
## 21654 NorthAmerica <=50K 0.000000 7.462215
## 21672 NorthAmerica <=50K 0.000000 0.000000
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## 24859 NorthAmerica >50K 0.000000 0.000000
## 24860 NorthAmerica >50K 0.000000 7.551187
## 24866 NorthAmerica <=50K 0.000000 0.000000
## 24875 NorthAmerica >50K 0.000000 0.000000
## 24877 Europe <=50K 0.000000 0.000000
## 24878 Europe <=50K 0.000000 0.000000
## 24882 NorthAmerica <=50K 0.000000 0.000000
## 24887 NorthAmerica <=50K 0.000000 0.000000
## 24900 NorthAmerica <=50K 0.000000 0.000000
## 24901 NorthAmerica >50K 0.000000 0.000000
## 24905 NorthAmerica <=50K 0.000000 0.000000
## 24915 NorthAmerica <=50K 0.000000 0.000000
## 24916 NorthAmerica <=50K 0.000000 7.303843
## 24935 NorthAmerica <=50K 0.000000 0.000000
## 24954 NorthAmerica <=50K 0.000000 0.000000
## 24955 NorthAmerica <=50K 0.000000 0.000000
## 24958 NorthAmerica <=50K 0.000000 0.000000
## 24959 NorthAmerica <=50K 0.000000 0.000000
## 24964 NorthAmerica <=50K 0.000000 0.000000
## 24967 NorthAmerica <=50K 0.000000 0.000000
## 24977 NorthAmerica <=50K 0.000000 0.000000
## 24978 NorthAmerica <=50K 0.000000 0.000000
## 24981 CentrAmerica <=50K 0.000000 0.000000
## 24982 SAsia <=50K 0.000000 0.000000
## 24983 NorthAmerica <=50K 6.962243 0.000000
## 24992 NorthAmerica <=50K 0.000000 0.000000
## 24993 NorthAmerica <=50K 0.000000 0.000000
## 24997 NorthAmerica <=50K 0.000000 0.000000
## 24998 NorthAmerica <=50K 0.000000 0.000000
## 25002 NorthAmerica <=50K 0.000000 0.000000
## 25004 NorthAmerica <=50K 0.000000 0.000000
## 25007 NorthAmerica >50K 8.552367 0.000000
## 25026 Europe <=50K 8.051341 0.000000
## 25037 NorthAmerica <=50K 0.000000 0.000000
## 25040 NorthAmerica >50K 0.000000 7.589842
## 25042 NorthAmerica <=50K 0.000000 0.000000
## 25044 NorthAmerica <=50K 0.000000 0.000000
## 25045 NorthAmerica <=50K 0.000000 0.000000
## 25053 NorthAmerica <=50K 0.000000 0.000000
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## 25059 NorthAmerica <=50K 0.000000 0.000000
## 25072 NorthAmerica <=50K 0.000000 0.000000
## 25090 NorthAmerica <=50K 0.000000 0.000000
## 25092 NorthAmerica <=50K 8.444838 0.000000
## 25093 NorthAmerica <=50K 0.000000 0.000000
## 25102 NorthAmerica <=50K 0.000000 0.000000
## 25109 NorthAmerica >50K 8.895493 0.000000
## 25118 NorthAmerica <=50K 0.000000 0.000000
## 25119 EAsia <=50K 0.000000 7.303843
## 25125 NorthAmerica <=50K 0.000000 0.000000
## 25135 NorthAmerica <=50K 0.000000 0.000000
## 25136 NorthAmerica >50K 0.000000 7.589842
## 25141 NorthAmerica <=50K 0.000000 0.000000
## 25143 NorthAmerica <=50K 0.000000 0.000000
## 25149 NorthAmerica <=50K 0.000000 0.000000
## 25150 NorthAmerica >50K 0.000000 0.000000
## 25151 NorthAmerica >50K 0.000000 7.355641
## 25153 NorthAmerica <=50K 0.000000 0.000000
## 25162 NorthAmerica <=50K 7.883823 0.000000
## 25163 NorthAmerica <=50K 0.000000 0.000000
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## 25201 NorthAmerica <=50K 0.000000 0.000000
## 25210 NorthAmerica >50K 0.000000 0.000000
## 25217 NorthAmerica <=50K 7.685703 0.000000
## 25220 NorthAmerica <=50K 0.000000 0.000000
## 25231 NorthAmerica <=50K 8.444838 0.000000
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## 25239 NorthAmerica <=50K 0.000000 0.000000
## 25246 NorthAmerica <=50K 0.000000 0.000000
## 25251 NorthAmerica >50K 8.552367 0.000000
## 25255 NorthAmerica <=50K 0.000000 0.000000
## 25256 NorthAmerica >50K 9.617471 0.000000
## 25258 NorthAmerica <=50K 0.000000 0.000000
## 25260 NorthAmerica >50K 9.617471 0.000000
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## 25297 CentrAmerica <=50K 0.000000 0.000000
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## 25318 NorthAmerica <=50K 0.000000 0.000000
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## 25329 NorthAmerica <=50K 0.000000 0.000000
## 25330 NorthAmerica <=50K 0.000000 0.000000
## 25336 NorthAmerica >50K 0.000000 7.589842
## 25344 NorthAmerica <=50K 0.000000 0.000000
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## 25349 NorthAmerica <=50K 0.000000 0.000000
## 25352 NorthAmerica <=50K 0.000000 0.000000
## 25357 NorthAmerica >50K 9.617471 0.000000
## 25363 NorthAmerica <=50K 8.109526 0.000000
## 25376 NorthAmerica <=50K 0.000000 0.000000
## 25377 SAsia <=50K 0.000000 0.000000
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## 25382 NorthAmerica >50K 0.000000 0.000000
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## 25384 NorthAmerica >50K 0.000000 0.000000
## 25404 NorthAmerica <=50K 0.000000 0.000000
## 25409 NorthAmerica <=50K 0.000000 7.395722
## 25412 NorthAmerica >50K 0.000000 0.000000
## 25413 NorthAmerica <=50K 0.000000 0.000000
## 25421 NorthAmerica <=50K 7.685703 0.000000
## 25422 NorthAmerica <=50K 0.000000 0.000000
## 25427 NorthAmerica >50K 0.000000 0.000000
## 25430 NorthAmerica >50K 8.895493 0.000000
## 25431 NorthAmerica <=50K 0.000000 0.000000
## 25439 NorthAmerica >50K 0.000000 7.543273
## 25441 NorthAmerica <=50K 0.000000 0.000000
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## 25456 NorthAmerica <=50K 0.000000 0.000000
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## 25479 CentrAmerica <=50K 0.000000 0.000000
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## 25490 NorthAmerica <=50K 0.000000 0.000000
## 25492 CentrAmerica >50K 0.000000 7.774015
## 25501 NorthAmerica <=50K 0.000000 0.000000
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## 25542 NorthAmerica <=50K 0.000000 0.000000
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## 25570 CentrAmerica <=50K 7.855932 0.000000
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## 25603 NorthAmerica <=50K 0.000000 7.722678
## 25611 NorthAmerica >50K 0.000000 0.000000
## 25624 NorthAmerica >50K 9.617471 0.000000
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## 25690 NorthAmerica <=50K 0.000000 0.000000
## 25695 NorthAmerica >50K 8.552367 0.000000
## 25697 EAsia <=50K 0.000000 0.000000
## 25702 NorthAmerica <=50K 0.000000 0.000000
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## 25788 NorthAmerica <=50K 0.000000 7.757906
## 25790 NorthAmerica >50K 9.617471 0.000000
## 25793 NorthAmerica <=50K 0.000000 0.000000
## 25794 NorthAmerica <=50K 0.000000 7.921173
## 25798 NorthAmerica <=50K 0.000000 0.000000
## 25801 NorthAmerica >50K 0.000000 7.589842
## 25804 NorthAmerica >50K 0.000000 0.000000
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## 25841 NorthAmerica >50K 8.895493 0.000000
## 25842 CentrAmerica >50K 11.512925 0.000000
## 25849 NorthAmerica <=50K 0.000000 0.000000
## 25850 NorthAmerica <=50K 0.000000 0.000000
## 25851 NorthAmerica >50K 8.895493 0.000000
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## 25900 EAsia <=50K 0.000000 0.000000
## 25901 NorthAmerica <=50K 0.000000 0.000000
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## 25910 NorthAmerica >50K 0.000000 0.000000
## 25915 NorthAmerica >50K 0.000000 7.589842
## 25917 NorthAmerica <=50K 8.150468 0.000000
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## 25922 NorthAmerica <=50K 0.000000 0.000000
## 25925 NorthAmerica <=50K 8.109526 0.000000
## 25930 USTerritory <=50K 0.000000 0.000000
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## 26070 NorthAmerica >50K 8.947546 0.000000
## 26071 NorthAmerica <=50K 0.000000 0.000000
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## 26078 NorthAmerica <=50K 0.000000 7.379632
## 26090 NorthAmerica >50K 0.000000 0.000000
## 26091 NorthAmerica <=50K 0.000000 0.000000
## 26092 EAsia <=50K 0.000000 0.000000
## 26099 NorthAmerica >50K 0.000000 0.000000
## 26100 NorthAmerica <=50K 0.000000 0.000000
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## 26146 NorthAmerica <=50K 0.000000 0.000000
## 26151 EAsia >50K 0.000000 0.000000
## 26153 SouthAmerica <=50K 0.000000 0.000000
## 26157 NorthAmerica <=50K 0.000000 0.000000
## 26161 Europe <=50K 0.000000 0.000000
## 26162 NorthAmerica >50K 0.000000 0.000000
## 26167 NorthAmerica <=50K 0.000000 0.000000
## 26169 NorthAmerica >50K 0.000000 0.000000
## 26177 NorthAmerica <=50K 0.000000 0.000000
## 26179 NorthAmerica >50K 9.261129 0.000000
## 26186 NorthAmerica <=50K 8.519989 0.000000
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## 26201 NorthAmerica <=50K 0.000000 0.000000
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## 26241 NorthAmerica <=50K 0.000000 0.000000
## 26246 NorthAmerica <=50K 7.684784 0.000000
## 26247 NorthAmerica <=50K 0.000000 0.000000
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## 26255 NorthAmerica <=50K 0.000000 0.000000
## 26263 NorthAmerica <=50K 0.000000 0.000000
## 26266 NorthAmerica <=50K 0.000000 7.250636
## 26274 NorthAmerica <=50K 0.000000 0.000000
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## 26290 NorthAmerica >50K 0.000000 0.000000
## 26292 NorthAmerica <=50K 0.000000 0.000000
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## 26312 NorthAmerica <=50K 0.000000 0.000000
## 26325 NorthAmerica <=50K 0.000000 7.379632
## 26327 NorthAmerica <=50K 0.000000 7.365180
## 26334 NorthAmerica <=50K 0.000000 0.000000
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## 26349 NorthAmerica >50K 0.000000 0.000000
## 26352 NorthAmerica <=50K 0.000000 0.000000
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## 26357 SAsia >50K 8.040447 0.000000
## 26378 CentrAmerica <=50K 0.000000 0.000000
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## 26422 NorthAmerica <=50K 0.000000 0.000000
## 26426 NorthAmerica <=50K 0.000000 0.000000
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## 26439 Europe <=50K 7.684784 0.000000
## 26444 NorthAmerica <=50K 0.000000 0.000000
## 26446 NorthAmerica <=50K 0.000000 0.000000
## 26454 NorthAmerica <=50K 0.000000 0.000000
## 26458 EAsia >50K 0.000000 7.589842
## 26459 NorthAmerica <=50K 0.000000 0.000000
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## 26499 NorthAmerica >50K 0.000000 7.543273
## 26501 NorthAmerica <=50K 0.000000 0.000000
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## 26550 NorthAmerica <=50K 0.000000 7.807103
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## 29966 NorthAmerica >50K 0.000000 0.000000
## 29967 NorthAmerica <=50K 0.000000 0.000000
## 29970 SouthAmerica <=50K 0.000000 0.000000
## 29975 NorthAmerica <=50K 0.000000 0.000000
## 29983 NorthAmerica <=50K 0.000000 7.379632
## 30005 NorthAmerica <=50K 0.000000 0.000000
## 30008 CentrAmerica <=50K 0.000000 0.000000
## 30013 NorthAmerica >50K 0.000000 0.000000
## 30021 NorthAmerica <=50K 0.000000 0.000000
## 30025 NorthAmerica <=50K 0.000000 0.000000
## 30030 NorthAmerica <=50K 0.000000 0.000000
## 30032 NorthAmerica >50K 0.000000 0.000000
## 30041 NorthAmerica <=50K 0.000000 0.000000
## 30053 NorthAmerica >50K 0.000000 0.000000
## 30056 NorthAmerica <=50K 0.000000 0.000000
## 30063 NorthAmerica <=50K 0.000000 0.000000
## 30066 CentrAmerica <=50K 8.051341 0.000000
## 30074 NorthAmerica <=50K 0.000000 7.450080
## 30086 NorthAmerica <=50K 0.000000 0.000000
## 30094 NorthAmerica >50K 0.000000 0.000000
## 30098 NorthAmerica <=50K 0.000000 0.000000
## 30099 NorthAmerica <=50K 0.000000 0.000000
## 30103 NorthAmerica <=50K 0.000000 0.000000
## 30107 NorthAmerica >50K 0.000000 0.000000
## 30115 NorthAmerica <=50K 0.000000 0.000000
## 30119 NorthAmerica >50K 8.895493 0.000000
## 30122 NorthAmerica <=50K 0.000000 0.000000
## 30124 NorthAmerica >50K 8.947546 0.000000
## 30125 NorthAmerica <=50K 0.000000 0.000000
## 30128 NorthAmerica >50K 9.261129 0.000000
## 30129 NorthAmerica <=50K 0.000000 0.000000
## 30130 NorthAmerica >50K 0.000000 0.000000
## 30132 NorthAmerica <=50K 0.000000 0.000000
## 30134 NorthAmerica <=50K 0.000000 0.000000
## 30147 NorthAmerica <=50K 8.109526 0.000000
## 30151 NorthAmerica <=50K 0.000000 0.000000
## 30154 NorthAmerica <=50K 0.000000 0.000000
## 30157 NorthAmerica <=50K 0.000000 0.000000
## 30158 NorthAmerica <=50K 0.000000 0.000000
## 30161 NorthAmerica <=50K 0.000000 0.000000
## 30163 NorthAmerica <=50K 8.319230 0.000000
## 30165 NorthAmerica <=50K 0.000000 0.000000
## 30169 CentrAmerica <=50K 0.000000 0.000000
## 30172 NorthAmerica <=50K 0.000000 0.000000
## 30188 NorthAmerica <=50K 0.000000 0.000000
## 30196 Europe <=50K 0.000000 0.000000
## 30200 NorthAmerica <=50K 0.000000 0.000000
## 30202 NorthAmerica <=50K 7.948032 0.000000
## 30211 NorthAmerica <=50K 0.000000 0.000000
## 30213 NorthAmerica >50K 8.473868 0.000000
## 30214 NorthAmerica <=50K 0.000000 0.000000
## 30215 NorthAmerica <=50K 0.000000 0.000000
## 30217 NorthAmerica >50K 8.895493 0.000000
## 30227 NorthAmerica <=50K 0.000000 0.000000
## 30228 NorthAmerica >50K 0.000000 0.000000
## 30237 NorthAmerica <=50K 0.000000 0.000000
## 30242 NorthAmerica <=50K 0.000000 0.000000
## 30258 NorthAmerica <=50K 0.000000 0.000000
## 30260 NorthAmerica >50K 8.947546 0.000000
## 30265 NorthAmerica <=50K 0.000000 0.000000
## 30266 NorthAmerica <=50K 0.000000 0.000000
## 30273 CentrAmerica <=50K 0.000000 0.000000
## 30274 NorthAmerica >50K 0.000000 7.551187
## 30278 NorthAmerica >50K 8.552367 0.000000
## 30283 NorthAmerica <=50K 0.000000 0.000000
## 30285 NorthAmerica >50K 0.000000 7.509883
## 30286 CentrAmerica <=50K 0.000000 0.000000
## 30291 NorthAmerica >50K 0.000000 0.000000
## 30305 NorthAmerica <=50K 0.000000 0.000000
## 30308 NorthAmerica <=50K 0.000000 7.379632
## 30315 NorthAmerica <=50K 0.000000 0.000000
## 30316 NorthAmerica >50K 0.000000 0.000000
## 30323 SouthAmerica <=50K 0.000000 0.000000
## 30325 NorthAmerica <=50K 0.000000 0.000000
## 30345 NorthAmerica <=50K 0.000000 0.000000
## 30348 SouthAmerica <=50K 0.000000 0.000000
## 30353 NorthAmerica >50K 0.000000 0.000000
## 30360 NorthAmerica <=50K 0.000000 0.000000
## 30362 NorthAmerica <=50K 0.000000 0.000000
## 30367 NorthAmerica <=50K 0.000000 0.000000
## 30375 NorthAmerica <=50K 0.000000 0.000000
## 30382 NorthAmerica <=50K 0.000000 0.000000
## 30385 NorthAmerica <=50K 0.000000 0.000000
## 30391 NorthAmerica <=50K 0.000000 0.000000
## 30394 NorthAmerica >50K 0.000000 0.000000
## 30395 NorthAmerica <=50K 0.000000 0.000000
## 30412 NorthAmerica <=50K 0.000000 0.000000
## 30414 NorthAmerica <=50K 0.000000 0.000000
## 30417 NorthAmerica <=50K 0.000000 0.000000
## 30422 NorthAmerica <=50K 7.967627 0.000000
## 30438 NorthAmerica <=50K 0.000000 0.000000
## 30459 NorthAmerica <=50K 0.000000 0.000000
## 30460 NorthAmerica <=50K 0.000000 0.000000
## 30468 NorthAmerica <=50K 0.000000 0.000000
## 30477 CentrAmerica >50K 0.000000 7.543273
## 30486 NorthAmerica >50K 0.000000 0.000000
## 30501 NorthAmerica <=50K 0.000000 0.000000
## 30514 NorthAmerica <=50K 0.000000 0.000000
## 30519 NorthAmerica <=50K 8.051341 0.000000
## 30522 NorthAmerica >50K 0.000000 0.000000
## 30529 CentrAmerica <=50K 0.000000 7.303843
## 30530 NorthAmerica <=50K 0.000000 0.000000
## 30531 NorthAmerica <=50K 0.000000 0.000000
## 30532 NorthAmerica <=50K 0.000000 0.000000
## 30539 NorthAmerica <=50K 0.000000 0.000000
## 30541 NorthAmerica <=50K 0.000000 0.000000
## 30543 NorthAmerica <=50K 0.000000 0.000000
## 30548 NorthAmerica <=50K 0.000000 0.000000
## 30549 NorthAmerica <=50K 0.000000 0.000000
## 30551 NorthAmerica <=50K 0.000000 0.000000
## 30553 NorthAmerica <=50K 0.000000 0.000000
## 30554 NorthAmerica <=50K 0.000000 0.000000
## 30557 NorthAmerica >50K 0.000000 0.000000
## 30558 NorthAmerica <=50K 0.000000 0.000000
## 30565 NorthAmerica >50K 0.000000 0.000000
## 30571 NorthAmerica <=50K 0.000000 0.000000
## 30583 CentrAmerica <=50K 0.000000 0.000000
## 30586 NorthAmerica >50K 0.000000 0.000000
## 30589 NorthAmerica <=50K 0.000000 0.000000
## 30600 NorthAmerica <=50K 0.000000 0.000000
## 30608 NorthAmerica <=50K 0.000000 0.000000
## 30612 CentrAmerica <=50K 0.000000 0.000000
## 30614 NorthAmerica <=50K 0.000000 0.000000
## 30617 NorthAmerica <=50K 0.000000 0.000000
## 30620 NorthAmerica <=50K 0.000000 0.000000
## 30621 NorthAmerica >50K 0.000000 0.000000
## 30622 NorthAmerica >50K 0.000000 0.000000
## 30628 NorthAmerica <=50K 0.000000 0.000000
## 30635 Europe <=50K 0.000000 0.000000
## 30641 NorthAmerica <=50K 0.000000 0.000000
## 30651 NorthAmerica <=50K 0.000000 0.000000
## 30659 NorthAmerica <=50K 0.000000 0.000000
## 30663 NorthAmerica <=50K 0.000000 0.000000
## 30668 NorthAmerica >50K 0.000000 0.000000
## 30675 NorthAmerica <=50K 0.000000 0.000000
## 30686 NorthAmerica <=50K 0.000000 0.000000
## 30689 NorthAmerica >50K 0.000000 0.000000
## 30692 NorthAmerica <=50K 0.000000 0.000000
## 30701 CentrAmerica >50K 0.000000 0.000000
## 30708 NorthAmerica <=50K 0.000000 0.000000
## 30713 NorthAmerica <=50K 0.000000 0.000000
## 30715 NorthAmerica <=50K 0.000000 0.000000
## 30719 NorthAmerica <=50K 0.000000 0.000000
## 30720 NorthAmerica <=50K 0.000000 0.000000
## 30725 NorthAmerica >50K 0.000000 0.000000
## 30729 NorthAmerica <=50K 8.813438 0.000000
## 30732 NorthAmerica <=50K 0.000000 0.000000
## 30735 NorthAmerica >50K 0.000000 0.000000
## 30737 NorthAmerica >50K 8.947546 0.000000
## 30742 NorthAmerica <=50K 0.000000 0.000000
## 30744 CentrAmerica <=50K 0.000000 0.000000
## 30750 NorthAmerica >50K 0.000000 7.543273
## 30751 NorthAmerica <=50K 0.000000 0.000000
## 30762 NorthAmerica <=50K 0.000000 7.588324
## 30764 NorthAmerica <=50K 0.000000 0.000000
## 30766 NorthAmerica <=50K 0.000000 0.000000
## 30767 NorthAmerica >50K 0.000000 0.000000
## 30781 NorthAmerica <=50K 0.000000 0.000000
## 30796 NorthAmerica <=50K 0.000000 0.000000
## 30797 NorthAmerica <=50K 0.000000 0.000000
## 30810 NorthAmerica <=50K 0.000000 0.000000
## 30817 NorthAmerica <=50K 0.000000 0.000000
## 30820 NorthAmerica <=50K 0.000000 0.000000
## 30823 NorthAmerica <=50K 0.000000 0.000000
## 30824 NorthAmerica <=50K 0.000000 7.462789
## 30833 NorthAmerica <=50K 6.818924 0.000000
## 30834 SAsia >50K 0.000000 0.000000
## 30836 NorthAmerica >50K 8.386401 0.000000
## 30845 NorthAmerica <=50K 0.000000 0.000000
## 30858 NorthAmerica <=50K 0.000000 0.000000
## 30860 NorthAmerica <=50K 0.000000 7.374629
## 30863 NorthAmerica <=50K 7.709308 0.000000
## 30865 NorthAmerica >50K 0.000000 0.000000
## 30871 NorthAmerica >50K 0.000000 0.000000
## 30877 NorthAmerica <=50K 0.000000 0.000000
## 30879 NorthAmerica <=50K 0.000000 0.000000
## 30884 NorthAmerica <=50K 0.000000 0.000000
## 30887 NorthAmerica <=50K 0.000000 0.000000
## 30889 NorthAmerica >50K 0.000000 0.000000
## 30892 NorthAmerica <=50K 0.000000 0.000000
## 30893 CentrAmerica <=50K 0.000000 0.000000
## 30894 NorthAmerica <=50K 0.000000 0.000000
## 30896 NorthAmerica <=50K 0.000000 0.000000
## 30898 NorthAmerica <=50K 0.000000 0.000000
## 30899 NorthAmerica <=50K 0.000000 0.000000
## 30900 NorthAmerica <=50K 0.000000 0.000000
## 30904 NorthAmerica <=50K 0.000000 0.000000
## 30905 NorthAmerica <=50K 0.000000 0.000000
## 30908 NorthAmerica <=50K 8.393216 0.000000
## 30918 NorthAmerica <=50K 0.000000 0.000000
## 30927 NorthAmerica <=50K 0.000000 0.000000
## 30931 NorthAmerica <=50K 0.000000 0.000000
## 30933 NorthAmerica >50K 0.000000 0.000000
## 30940 NorthAmerica <=50K 0.000000 0.000000
## 30948 NorthAmerica <=50K 0.000000 0.000000
## 30961 NorthAmerica >50K 0.000000 0.000000
## 30977 NorthAmerica <=50K 0.000000 0.000000
## 30978 NorthAmerica <=50K 0.000000 0.000000
## 30979 NorthAmerica <=50K 0.000000 0.000000
## 30980 NorthAmerica >50K 9.617471 0.000000
## 30990 NorthAmerica <=50K 0.000000 0.000000
## 30992 NorthAmerica <=50K 0.000000 0.000000
## 30995 NorthAmerica <=50K 0.000000 0.000000
## 30996 NorthAmerica <=50K 0.000000 0.000000
## 30998 NorthAmerica <=50K 0.000000 0.000000
## 30999 NorthAmerica <=50K 0.000000 0.000000
## 31001 NorthAmerica >50K 0.000000 0.000000
## 31007 NorthAmerica >50K 0.000000 7.543273
## 31012 NorthAmerica >50K 0.000000 0.000000
## 31013 NorthAmerica <=50K 0.000000 0.000000
## 31033 NorthAmerica <=50K 0.000000 0.000000
## 31034 CentrAmerica <=50K 8.135054 0.000000
## 31036 NorthAmerica <=50K 8.519989 0.000000
## 31042 NorthAmerica <=50K 0.000000 0.000000
## 31043 NorthAmerica >50K 0.000000 0.000000
## 31050 NorthAmerica <=50K 0.000000 0.000000
## 31051 NorthAmerica >50K 0.000000 0.000000
## 31057 NorthAmerica >50K 0.000000 7.355641
## 31063 NorthAmerica <=50K 7.786552 0.000000
## 31071 NorthAmerica >50K 0.000000 0.000000
## 31073 NorthAmerica <=50K 0.000000 0.000000
## 31079 NorthAmerica <=50K 7.685703 0.000000
## 31081 NorthAmerica >50K 0.000000 7.551187
## 31083 NorthAmerica >50K 0.000000 0.000000
## 31088 NorthAmerica <=50K 0.000000 0.000000
## 31089 NorthAmerica <=50K 0.000000 0.000000
## 31092 NorthAmerica <=50K 0.000000 0.000000
## 31093 NorthAmerica <=50K 0.000000 0.000000
## 31095 NorthAmerica <=50K 0.000000 0.000000
## 31097 NorthAmerica >50K 0.000000 0.000000
## 31100 NorthAmerica <=50K 0.000000 0.000000
## 31104 NorthAmerica <=50K 0.000000 0.000000
## 31111 NorthAmerica >50K 0.000000 0.000000
## 31112 NorthAmerica >50K 11.512925 0.000000
## 31113 NorthAmerica <=50K 0.000000 0.000000
## 31117 NorthAmerica <=50K 0.000000 0.000000
## 31120 NorthAmerica <=50K 0.000000 0.000000
## 31121 NorthAmerica <=50K 0.000000 0.000000
## 31125 NorthAmerica <=50K 0.000000 0.000000
## 31129 CentrAmerica >50K 0.000000 0.000000
## 31131 NorthAmerica >50K 0.000000 0.000000
## 31134 NorthAmerica <=50K 0.000000 0.000000
## 31136 NorthAmerica <=50K 8.444838 0.000000
## 31143 NorthAmerica <=50K 0.000000 0.000000
## 31155 NorthAmerica <=50K 0.000000 0.000000
## 31156 NorthAmerica <=50K 0.000000 0.000000
## 31162 NorthAmerica >50K 0.000000 0.000000
## 31168 NorthAmerica <=50K 0.000000 0.000000
## 31174 NorthAmerica <=50K 0.000000 0.000000
## 31183 NorthAmerica >50K 0.000000 0.000000
## 31186 EAsia >50K 0.000000 0.000000
## 31192 NorthAmerica <=50K 0.000000 0.000000
## 31194 NorthAmerica <=50K 0.000000 0.000000
## 31203 Europe <=50K 0.000000 0.000000
## 31212 NorthAmerica <=50K 0.000000 0.000000
## 31217 NorthAmerica >50K 9.617471 0.000000
## 31229 NorthAmerica <=50K 0.000000 0.000000
## 31238 NorthAmerica <=50K 0.000000 0.000000
## 31241 NorthAmerica <=50K 0.000000 0.000000
## 31250 NorthAmerica <=50K 0.000000 0.000000
## 31251 NorthAmerica >50K 0.000000 0.000000
## 31252 NorthAmerica <=50K 0.000000 0.000000
## 31254 NorthAmerica <=50K 0.000000 0.000000
## 31255 NorthAmerica >50K 0.000000 0.000000
## 31262 NorthAmerica <=50K 0.000000 0.000000
## 31267 NorthAmerica >50K 9.617471 0.000000
## 31272 NorthAmerica >50K 0.000000 0.000000
## 31276 NorthAmerica <=50K 0.000000 0.000000
## 31277 NorthAmerica <=50K 0.000000 0.000000
## 31278 NorthAmerica >50K 0.000000 7.355641
## 31283 NorthAmerica >50K 8.895493 0.000000
## 31286 NorthAmerica <=50K 0.000000 0.000000
## 31288 NorthAmerica <=50K 0.000000 7.462215
## 31291 NorthAmerica <=50K 0.000000 0.000000
## 31300 NorthAmerica <=50K 0.000000 0.000000
## 31305 NorthAmerica <=50K 0.000000 0.000000
## 31312 NorthAmerica >50K 8.895493 0.000000
## 31316 NorthAmerica <=50K 0.000000 0.000000
## 31318 NorthAmerica <=50K 0.000000 0.000000
## 31321 NorthAmerica <=50K 8.209308 0.000000
## 31323 NorthAmerica >50K 0.000000 0.000000
## 31325 NorthAmerica <=50K 0.000000 7.201171
## 31326 NorthAmerica <=50K 0.000000 0.000000
## 31329 NorthAmerica <=50K 0.000000 0.000000
## 31330 NorthAmerica <=50K 0.000000 0.000000
## 31331 NorthAmerica <=50K 0.000000 0.000000
## 31332 NorthAmerica >50K 0.000000 7.543273
## 31333 EAsia <=50K 0.000000 0.000000
## 31337 CentrAmerica <=50K 0.000000 0.000000
## 31343 NorthAmerica <=50K 0.000000 0.000000
## 31348 NorthAmerica <=50K 0.000000 0.000000
## 31351 NorthAmerica <=50K 0.000000 0.000000
## 31353 NorthAmerica <=50K 0.000000 0.000000
## 31359 NorthAmerica <=50K 0.000000 0.000000
## 31364 NorthAmerica <=50K 0.000000 0.000000
## 31377 Europe <=50K 0.000000 0.000000
## 31395 NorthAmerica <=50K 0.000000 0.000000
## 31404 NorthAmerica <=50K 0.000000 0.000000
## 31408 NorthAmerica <=50K 0.000000 0.000000
## 31409 NorthAmerica <=50K 0.000000 0.000000
## 31411 NorthAmerica <=50K 0.000000 0.000000
## 31412 NorthAmerica >50K 0.000000 0.000000
## 31415 NorthAmerica <=50K 0.000000 0.000000
## 31416 NorthAmerica <=50K 0.000000 0.000000
## 31421 NorthAmerica >50K 0.000000 0.000000
## 31428 NorthAmerica <=50K 0.000000 0.000000
## 31431 NorthAmerica >50K 0.000000 0.000000
## 31432 NorthAmerica >50K 0.000000 0.000000
## 31438 NorthAmerica <=50K 0.000000 0.000000
## 31441 NorthAmerica <=50K 0.000000 0.000000
## 31446 NorthAmerica <=50K 0.000000 0.000000
## 31456 NorthAmerica <=50K 0.000000 0.000000
## 31459 NorthAmerica <=50K 0.000000 0.000000
## 31461 NorthAmerica <=50K 0.000000 0.000000
## 31462 NorthAmerica >50K 0.000000 0.000000
## 31469 CentrAmerica >50K 8.895493 0.000000
## 31470 NorthAmerica >50K 0.000000 0.000000
## 31477 NorthAmerica <=50K 0.000000 0.000000
## 31482 NorthAmerica >50K 0.000000 0.000000
## 31500 NorthAmerica <=50K 0.000000 0.000000
## 31505 NorthAmerica <=50K 8.444838 0.000000
## 31515 NorthAmerica <=50K 8.109526 0.000000
## 31518 NorthAmerica <=50K 0.000000 0.000000
## 31519 NorthAmerica <=50K 0.000000 0.000000
## 31525 NorthAmerica >50K 0.000000 0.000000
## 31527 NorthAmerica <=50K 0.000000 0.000000
## 31535 NorthAmerica >50K 0.000000 0.000000
## 31541 NorthAmerica <=50K 0.000000 0.000000
## 31542 NorthAmerica <=50K 0.000000 0.000000
## 31552 NorthAmerica <=50K 0.000000 0.000000
## 31553 NorthAmerica <=50K 0.000000 0.000000
## 31556 CentrAmerica <=50K 0.000000 0.000000
## 31562 NorthAmerica >50K 0.000000 7.551187
## 31569 NorthAmerica <=50K 0.000000 0.000000
## 31570 NorthAmerica <=50K 0.000000 0.000000
## 31572 NorthAmerica <=50K 0.000000 0.000000
## 31580 NorthAmerica <=50K 0.000000 0.000000
## 31587 NorthAmerica <=50K 0.000000 0.000000
## 31591 NorthAmerica <=50K 0.000000 0.000000
## 31592 NorthAmerica >50K 0.000000 0.000000
## 31594 NorthAmerica <=50K 0.000000 0.000000
## 31599 NorthAmerica <=50K 0.000000 0.000000
## 31600 NorthAmerica <=50K 0.000000 0.000000
## 31602 NorthAmerica <=50K 0.000000 0.000000
## 31605 NorthAmerica >50K 0.000000 0.000000
## 31610 NorthAmerica >50K 0.000000 0.000000
## 31619 NorthAmerica >50K 8.947546 0.000000
## 31622 NorthAmerica <=50K 0.000000 0.000000
## 31629 NorthAmerica >50K 0.000000 0.000000
## 31634 NorthAmerica >50K 0.000000 7.589842
## 31635 EAsia <=50K 0.000000 0.000000
## 31641 NorthAmerica <=50K 0.000000 0.000000
## 31646 NorthAmerica >50K 8.947546 0.000000
## 31665 NorthAmerica <=50K 0.000000 0.000000
## 31667 NorthAmerica <=50K 0.000000 0.000000
## 31673 NorthAmerica <=50K 0.000000 0.000000
## 31677 NorthAmerica <=50K 0.000000 0.000000
## 31678 NorthAmerica <=50K 0.000000 0.000000
## 31681 NorthAmerica >50K 0.000000 0.000000
## 31695 NorthAmerica <=50K 0.000000 0.000000
## 31697 NorthAmerica >50K 0.000000 0.000000
## 31702 CentrAmerica <=50K 0.000000 0.000000
## 31706 NorthAmerica <=50K 0.000000 0.000000
## 31715 NorthAmerica >50K 0.000000 0.000000
## 31719 NorthAmerica <=50K 0.000000 0.000000
## 31726 NorthAmerica <=50K 0.000000 0.000000
## 31740 NorthAmerica <=50K 0.000000 0.000000
## 31744 NorthAmerica >50K 0.000000 0.000000
## 31746 NorthAmerica >50K 0.000000 0.000000
## 31748 NorthAmerica >50K 0.000000 0.000000
## 31756 NorthAmerica <=50K 0.000000 0.000000
## 31757 NorthAmerica <=50K 0.000000 0.000000
## 31760 NorthAmerica <=50K 0.000000 0.000000
## 31761 NorthAmerica <=50K 0.000000 0.000000
## 31762 NorthAmerica >50K 0.000000 0.000000
## 31764 NorthAmerica >50K 0.000000 0.000000
## 31771 EAsia <=50K 0.000000 0.000000
## 31795 NorthAmerica >50K 0.000000 0.000000
## 31798 NorthAmerica <=50K 0.000000 0.000000
## 31801 NorthAmerica <=50K 8.135054 0.000000
## 31803 NorthAmerica >50K 9.617471 0.000000
## 31806 NorthAmerica <=50K 0.000000 0.000000
## 31808 NorthAmerica >50K 0.000000 0.000000
## 31809 NorthAmerica <=50K 0.000000 0.000000
## 31810 NorthAmerica >50K 8.552367 0.000000
## 31813 NorthAmerica <=50K 0.000000 0.000000
## 31814 NorthAmerica <=50K 0.000000 0.000000
## 31821 NorthAmerica <=50K 0.000000 0.000000
## 31838 NorthAmerica <=50K 0.000000 0.000000
## 31840 NorthAmerica <=50K 0.000000 0.000000
## 31846 NorthAmerica >50K 0.000000 7.551187
## 31852 NorthAmerica >50K 8.913416 0.000000
## 31864 NorthAmerica <=50K 0.000000 0.000000
## 31867 NorthAmerica <=50K 0.000000 0.000000
## 31868 NorthAmerica <=50K 0.000000 0.000000
## 31880 NorthAmerica <=50K 0.000000 0.000000
## 31885 NorthAmerica >50K 0.000000 0.000000
## 31886 NorthAmerica <=50K 0.000000 0.000000
## 31890 NorthAmerica <=50K 0.000000 0.000000
## 31894 NorthAmerica <=50K 0.000000 5.780744
## 31902 NorthAmerica <=50K 0.000000 0.000000
## 31903 Europe <=50K 0.000000 0.000000
## 31909 NorthAmerica <=50K 0.000000 0.000000
## 31913 NorthAmerica <=50K 0.000000 0.000000
## 31918 NorthAmerica <=50K 0.000000 0.000000
## 31920 CentrAmerica <=50K 0.000000 0.000000
## 31925 NorthAmerica <=50K 0.000000 0.000000
## 31927 NorthAmerica <=50K 0.000000 0.000000
## 31929 NorthAmerica <=50K 0.000000 0.000000
## 31932 NorthAmerica <=50K 0.000000 0.000000
## 31944 NorthAmerica <=50K 0.000000 0.000000
## 31948 NorthAmerica <=50K 0.000000 0.000000
## 31954 NorthAmerica >50K 0.000000 7.522400
## 31957 NorthAmerica <=50K 0.000000 0.000000
## 31958 NorthAmerica <=50K 0.000000 0.000000
## 31965 NorthAmerica <=50K 0.000000 0.000000
## 31972 NorthAmerica <=50K 0.000000 0.000000
## 31976 NorthAmerica <=50K 0.000000 0.000000
## 31979 NorthAmerica >50K 0.000000 0.000000
## 31988 NorthAmerica <=50K 0.000000 0.000000
## 31990 NorthAmerica <=50K 0.000000 0.000000
## 31992 NorthAmerica <=50K 0.000000 0.000000
## 32001 NorthAmerica <=50K 0.000000 0.000000
## 32004 NorthAmerica <=50K 0.000000 0.000000
## 32009 NorthAmerica <=50K 0.000000 0.000000
## 32016 NorthAmerica <=50K 0.000000 0.000000
## 32020 NorthAmerica <=50K 0.000000 0.000000
## 32022 NorthAmerica <=50K 0.000000 0.000000
## 32024 NorthAmerica <=50K 0.000000 0.000000
## 32025 NorthAmerica >50K 0.000000 0.000000
## 32026 NorthAmerica >50K 0.000000 0.000000
## 32028 NorthAmerica >50K 9.061260 0.000000
## 32032 NorthAmerica <=50K 0.000000 0.000000
## 32044 NorthAmerica <=50K 0.000000 0.000000
## 32050 NorthAmerica >50K 0.000000 0.000000
## 32055 NorthAmerica <=50K 0.000000 0.000000
## 32056 NorthAmerica <=50K 0.000000 0.000000
## 32060 NorthAmerica <=50K 0.000000 0.000000
## 32066 NorthAmerica <=50K 0.000000 0.000000
## 32075 Europe <=50K 0.000000 0.000000
## 32079 NorthAmerica <=50K 0.000000 0.000000
## 32080 NorthAmerica >50K 0.000000 0.000000
## 32083 NorthAmerica <=50K 0.000000 0.000000
## 32085 NorthAmerica >50K 0.000000 0.000000
## 32087 NorthAmerica <=50K 0.000000 0.000000
## 32089 NorthAmerica <=50K 0.000000 0.000000
## 32090 NorthAmerica <=50K 0.000000 0.000000
## 32093 NorthAmerica >50K 0.000000 7.543273
## 32107 NorthAmerica <=50K 0.000000 0.000000
## 32114 NorthAmerica <=50K 0.000000 0.000000
## 32115 NorthAmerica >50K 0.000000 0.000000
## 32118 NorthAmerica >50K 0.000000 0.000000
## 32124 NorthAmerica >50K 9.514216 0.000000
## 32126 NorthAmerica >50K 0.000000 0.000000
## 32131 NorthAmerica >50K 0.000000 0.000000
## 32134 NorthAmerica >50K 0.000000 0.000000
## 32136 NorthAmerica >50K 0.000000 7.722678
## 32137 NorthAmerica <=50K 0.000000 0.000000
## 32140 NorthAmerica >50K 9.617471 0.000000
## 32142 Europe <=50K 0.000000 0.000000
## 32153 NorthAmerica <=50K 0.000000 0.000000
## 32155 NorthAmerica <=50K 0.000000 0.000000
## 32159 NorthAmerica <=50K 0.000000 0.000000
## 32161 NorthAmerica <=50K 0.000000 0.000000
## 32166 NorthAmerica <=50K 0.000000 0.000000
## 32167 NorthAmerica <=50K 0.000000 0.000000
## 32174 NorthAmerica >50K 0.000000 0.000000
## 32180 NorthAmerica >50K 8.552367 0.000000
## 32181 NorthAmerica <=50K 0.000000 0.000000
## 32189 NorthAmerica <=50K 0.000000 0.000000
## 32193 NorthAmerica <=50K 0.000000 0.000000
## 32215 CentrAmerica >50K 0.000000 0.000000
## 32216 NorthAmerica <=50K 0.000000 0.000000
## 32217 NorthAmerica >50K 0.000000 7.551187
## 32218 NorthAmerica <=50K 0.000000 0.000000
## 32225 NorthAmerica <=50K 0.000000 0.000000
## 32226 NorthAmerica <=50K 0.000000 0.000000
## 32230 CentrAmerica <=50K 0.000000 0.000000
## 32234 NorthAmerica <=50K 0.000000 0.000000
## 32239 NorthAmerica >50K 11.512925 0.000000
## 32240 NorthAmerica <=50K 0.000000 0.000000
## 32243 EAsia <=50K 0.000000 0.000000
## 32247 NorthAmerica >50K 0.000000 0.000000
## 32249 NorthAmerica <=50K 0.000000 0.000000
## 32251 NorthAmerica >50K 0.000000 7.589842
## 32255 CentrAmerica >50K 0.000000 7.303843
## 32259 NorthAmerica >50K 0.000000 0.000000
## 32262 NorthAmerica <=50K 0.000000 0.000000
## 32266 NorthAmerica >50K 0.000000 0.000000
## 32287 NorthAmerica <=50K 0.000000 0.000000
## 32288 NorthAmerica >50K 0.000000 0.000000
## 32292 NorthAmerica <=50K 0.000000 0.000000
## 32308 CentrAmerica >50K 0.000000 7.589842
## 32310 NorthAmerica <=50K 0.000000 0.000000
## 32318 NorthAmerica <=50K 0.000000 0.000000
## 32320 NorthAmerica <=50K 0.000000 0.000000
## 32321 NorthAmerica <=50K 0.000000 0.000000
## 32326 NorthAmerica <=50K 0.000000 0.000000
## 32333 NorthAmerica <=50K 0.000000 0.000000
## 32335 NorthAmerica <=50K 0.000000 0.000000
## 32338 Europe <=50K 0.000000 0.000000
## 32341 NorthAmerica <=50K 0.000000 0.000000
## 32343 NorthAmerica <=50K 0.000000 0.000000
## 32352 NorthAmerica <=50K 0.000000 0.000000
## 32357 NorthAmerica <=50K 0.000000 0.000000
## 32365 NorthAmerica <=50K 0.000000 0.000000
## 32374 NorthAmerica <=50K 0.000000 0.000000
## 32387 NorthAmerica >50K 0.000000 0.000000
## 32396 NorthAmerica <=50K 0.000000 0.000000
## 32399 NorthAmerica <=50K 0.000000 0.000000
## 32413 NorthAmerica <=50K 0.000000 0.000000
## 32421 NorthAmerica <=50K 0.000000 0.000000
## 32426 NorthAmerica <=50K 0.000000 0.000000
## 32428 NorthAmerica <=50K 0.000000 0.000000
## 32431 NorthAmerica <=50K 0.000000 0.000000
## 32435 NorthAmerica <=50K 8.915164 0.000000
## 32436 NorthAmerica <=50K 0.000000 0.000000
## 32438 NorthAmerica <=50K 0.000000 0.000000
## 32440 NorthAmerica <=50K 0.000000 0.000000
## 32443 NorthAmerica <=50K 0.000000 0.000000
## 32456 NorthAmerica >50K 0.000000 0.000000
## 32461 NorthAmerica <=50K 0.000000 0.000000
## 32472 NorthAmerica <=50K 0.000000 0.000000
## 32475 NorthAmerica >50K 0.000000 0.000000
## 32478 NorthAmerica <=50K 0.000000 0.000000
## 32479 NorthAmerica <=50K 0.000000 0.000000
## 32485 NorthAmerica <=50K 0.000000 0.000000
## 32491 NorthAmerica <=50K 0.000000 0.000000
## 32502 NorthAmerica <=50K 0.000000 0.000000
## 32508 NorthAmerica <=50K 0.000000 0.000000
## 32513 NorthAmerica <=50K 0.000000 0.000000
## 32518 NorthAmerica <=50K 0.000000 0.000000
## 32520 NorthAmerica >50K 0.000000 0.000000
## 32522 NorthAmerica <=50K 0.000000 0.000000
## 32525 NorthAmerica <=50K 0.000000 0.000000
## 32526 CentrAmerica <=50K 0.000000 0.000000
## 32535 NorthAmerica <=50K 0.000000 0.000000
## 32550 NorthAmerica <=50K 0.000000 0.000000
## 32558 NorthAmerica >50K 0.000000 0.000000# For continuous check
index <- sample(1:nrow(adult_dum), size = round(nrow(adult_dum) * 0.8))
# Splitting
training_set_LR <- adult_dum[index, ]
testing_set_LR <- adult_dum[-index, ]
#Outliers removed
index <- sample(1:nrow(adult.out), size = round(nrow(adult.out) * 0.8))
# Splitting
training_set_out <- adult.out[index, ]
testing_set_out <- adult.out[-index, ]Initial test of the model without changes
- Result summary: A review of the 1) full logistic, 2) influential outliers removed, and 3) the One-Hot-Encoded variables models show slight difference in the confusion matrix results and the significance levels. Model4 outperforms model3 and model5 slightly, and all tests have similar AUROC scores and achieve a balance of approximately 80 percent on Sensitivity and Specificity. The one-hot-encoded model found many more variables to be significant, but had lower performance overall.
# Fit a logistic regression model
model3 <- glm(income ~ ., data = training_set, family = binomial)
# Confidence interval
# confint(model3)
# Summarize the final selected model
summary(model3)##
## Call:
## glm(formula = income ~ ., family = binomial, data = training_set)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -21.898954 225.382927 -0.097 0.922597
## age 0.025339 0.001783 14.209 < 2e-16 ***
## workclassFedGov 13.556320 225.382382 0.060 0.952038
## workclassLocGov 12.854092 225.382372 0.057 0.954519
## workclassNeverWorked -10.624244 288.906847 -0.037 0.970665
## workclassPrivate 13.038526 225.382363 0.058 0.953868
## workclassSelfEmpInc 13.292266 225.382379 0.059 0.952971
## workclassSelfEmpNotInc 12.567980 225.382370 0.056 0.955531
## workclassStateGov 12.802172 225.382381 0.057 0.954703
## education_levelhighsch -0.129442 0.067633 -1.914 0.055635 .
## education_levelmaster 0.166800 0.124686 1.338 0.180973
## education_levelno_edu -10.219102 115.190388 -0.089 0.929309
## education_levelphd 0.208181 0.220107 0.946 0.344241
## education_levelprimary 0.064266 0.344841 0.186 0.852158
## education_levelsecondary -0.453945 0.278697 -1.629 0.103353
## education_levelundergrad 0.069485 0.090666 0.766 0.443448
## years_education 0.252418 0.026951 9.366 < 2e-16 ***
## marital_statusmarried 0.603216 0.181376 3.326 0.000882 ***
## marital_statusnotmarried -0.543486 0.088941 -6.111 9.93e-10 ***
## marital_statuswidowed 0.069972 0.162197 0.431 0.666176
## occupationclerical 1.089924 0.155985 6.987 2.80e-12 ***
## occupationhighskill 1.636805 0.140002 11.691 < 2e-16 ***
## occupationlowskill 0.253068 0.163050 1.552 0.120640
## occupationmidskill 1.070199 0.141919 7.541 4.67e-14 ***
## occupationother 13.437870 225.382424 0.060 0.952456
## household_rolewife 1.303010 0.109743 11.873 < 2e-16 ***
## household_roletenant -0.956238 0.178110 -5.369 7.93e-08 ***
## household_rolesingle_occ -1.164939 0.198471 -5.870 4.37e-09 ***
## household_rolerelative -1.505985 0.255536 -5.893 3.78e-09 ***
## household_roledepchild -1.968558 0.219054 -8.987 < 2e-16 ***
## racewhite 0.282146 0.280508 1.006 0.314493
## raceblack 0.242802 0.289683 0.838 0.401937
## racenative -0.141462 0.366415 -0.386 0.699445
## raceAsian_Pac_Islander 0.347761 0.314142 1.107 0.268285
## gendermale 0.809518 0.082954 9.759 < 2e-16 ***
## hours_per_week 0.030392 0.001767 17.204 < 2e-16 ***
## native_regionEAsia 0.376252 0.229222 1.641 0.100709
## native_regionEurope 0.449932 0.183403 2.453 0.014158 *
## native_regionNorthAmerica 0.438229 0.122056 3.590 0.000330 ***
## native_regionSAsia -0.050890 0.304402 -0.167 0.867228
## native_regionSouthAmerica -0.706212 0.347370 -2.033 0.042050 *
## native_regionUSTerritory -0.158507 0.416619 -0.380 0.703603
## capital_gain_log 0.211433 0.007256 29.141 < 2e-16 ***
## capital_loss_log 0.155426 0.010570 14.704 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28679 on 26049 degrees of freedom
## Residual deviance: 17139 on 26006 degrees of freedom
## AIC: 17227
##
## Number of Fisher Scoring iterations: 13# Check for multicollinearity
vif(model3) ## GVIF Df GVIF^(1/(2*Df))
## age 1.262791e+00 1 1.123740
## workclass 5.746624e+06 7 3.039591
## education_level 1.174382e+01 7 1.192381
## years_education 1.067439e+01 1 3.267169
## marital_status 1.891728e+01 3 1.632337
## occupation 7.379591e+06 5 4.861869
## household_role 4.229618e+01 5 1.454220
## race 2.304074e+00 4 1.109972
## gender 2.791612e+00 1 1.670812
## hours_per_week 1.137054e+00 1 1.066328
## native_region 2.295988e+00 6 1.071719
## capital_gain_log 1.031097e+00 1 1.015429
## capital_loss_log 1.012801e+00 1 1.006380# Perform Hosmer-Lemeshow goodness of fit test
# Note: A p-value less than 0.05 typically suggests that the model does not fit the data well.
hoslem.test(training_set$income, fitted(model3))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: training_set$income, fitted(model3)
## X-squared = 26050, df = 8, p-value < 2.2e-16# hoslem.test(training_set_LR$income, fitted(model4)) # dummied variables - test comparison
# Make predictions
probabilities <- model3 %>% predict(testing_set, type = "response")
predicted.classes <- ifelse(probabilities > 0.26, ">50K", "<=50K")
# Model accuracy
accuracy <- mean(predicted.classes==testing_set$income)
# confusion matrix
tb <- table(predicted.classes, testing_set$income)
tb##
## predicted.classes <=50K >50K
## <=50K 3947 306
## >50K 962 1297# Calculate Sensitivity (True Positive Rate)
sensitivity <- tb[2, 2] / (tb[2, 2] + tb[1, 2])
# Calculate Specificity (True Negative Rate)
specificity <- tb[1, 1] / (tb[1, 1] + tb[2, 1])
# Calculate Prevalence
prevalence <- (tb[1, 2] + tb[2, 2]) / sum(tb)
# Calculate PPV (Positive Predictive Value)
ppv <- tb[2, 2] / (tb[2, 2] + tb[2, 1])
# Calculate NPV (Negative Predictive Value)
npv <- tb[1, 1] / (tb[1, 1] + tb[1, 2])
# Calculate AUROC
roc_obj <- roc(testing_set$income, probabilities)## Setting levels: control = <=50K, case = >50K## Setting direction: controls < casesauroc <- auc(roc_obj)
# Print the metrics
cat("accuracy:", accuracy, "\n")## accuracy: 0.8052826cat("Sensitivity:", sensitivity, "\n")## Sensitivity: 0.8091079cat("Specificity:", specificity, "\n")## Specificity: 0.8040334cat("Prevalence:", prevalence, "\n")## Prevalence: 0.2461609cat("PPV:", ppv, "\n")## PPV: 0.5741479cat("NPV:", npv, "\n")## NPV: 0.9280508cat("AUROC:", auroc, "\n")## AUROC: 0.8945609################## glm test with influential outliers removed ##############################
cat("\n\n----------------influential outliers removed--------------\n\n")##
##
## ----------------influential outliers removed--------------# Fit a logistic regression model
model4 <- glm(income ~ ., data = training_set_out, family = binomial)
# Confidence interval
# confint(model3)
# Summarize the final selected model
summary(model4)##
## Call:
## glm(formula = income ~ ., family = binomial, data = training_set_out)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -21.572497 254.007563 -0.085 0.932318
## age 0.025650 0.001782 14.398 < 2e-16 ***
## workclassFedGov 12.887309 254.007043 0.051 0.959536
## workclassLocGov 12.148596 254.007034 0.048 0.961853
## workclassNeverWorked -10.624428 290.331209 -0.037 0.970809
## workclassPrivate 12.327726 254.007025 0.049 0.961291
## workclassSelfEmpInc 12.534293 254.007040 0.049 0.960643
## workclassSelfEmpNotInc 11.863703 254.007032 0.047 0.962747
## workclassStateGov 12.048909 254.007042 0.047 0.962166
## education_levelhighsch -0.118089 0.067521 -1.749 0.080304 .
## education_levelmaster 0.221801 0.124468 1.782 0.074751 .
## education_levelno_edu -10.423748 115.347362 -0.090 0.927995
## education_levelphd 0.369327 0.218568 1.690 0.091075 .
## education_levelprimary 0.036119 0.344656 0.105 0.916536
## education_levelsecondary -0.387754 0.280117 -1.384 0.166280
## education_levelundergrad 0.077482 0.090763 0.854 0.393289
## years_education 0.253812 0.026904 9.434 < 2e-16 ***
## marital_statusmarried 0.781869 0.180984 4.320 1.56e-05 ***
## marital_statusnotmarried -0.455434 0.089140 -5.109 3.24e-07 ***
## marital_statuswidowed 0.201669 0.160488 1.257 0.208898
## occupationclerical 0.911643 0.151997 5.998 2.00e-09 ***
## occupationhighskill 1.502355 0.135967 11.049 < 2e-16 ***
## occupationlowskill 0.130073 0.158943 0.818 0.413152
## occupationmidskill 0.910502 0.137907 6.602 4.05e-11 ***
## occupationother 12.576305 254.007078 0.050 0.960512
## household_rolewife 1.290261 0.109616 11.771 < 2e-16 ***
## household_roletenant -0.830681 0.176768 -4.699 2.61e-06 ***
## household_rolesingle_occ -1.146927 0.198584 -5.776 7.67e-09 ***
## household_rolerelative -1.207740 0.233052 -5.182 2.19e-07 ***
## household_roledepchild -1.972228 0.222809 -8.852 < 2e-16 ***
## racewhite 0.591075 0.300803 1.965 0.049416 *
## raceblack 0.548002 0.309083 1.773 0.076230 .
## racenative -0.037869 0.385320 -0.098 0.921711
## raceAsian_Pac_Islander 0.845747 0.331458 2.552 0.010723 *
## gendermale 0.804968 0.082804 9.721 < 2e-16 ***
## hours_per_week 0.030229 0.001774 17.040 < 2e-16 ***
## native_regionEAsia 0.175254 0.230713 0.760 0.447483
## native_regionEurope 0.543644 0.186354 2.917 0.003531 **
## native_regionNorthAmerica 0.460419 0.122350 3.763 0.000168 ***
## native_regionSAsia -0.239784 0.288012 -0.833 0.405100
## native_regionSouthAmerica -0.701492 0.331371 -2.117 0.034265 *
## native_regionUSTerritory -0.239327 0.432480 -0.553 0.580002
## capital_gain_log 0.208900 0.007213 28.961 < 2e-16 ***
## capital_loss_log 0.144352 0.010457 13.804 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28594 on 26037 degrees of freedom
## Residual deviance: 17146 on 25994 degrees of freedom
## AIC: 17234
##
## Number of Fisher Scoring iterations: 13# Check for multicollinearity
vif(model4) ## GVIF Df GVIF^(1/(2*Df))
## age 1.272281e+00 1 1.127955
## workclass 7.483914e+06 7 3.097485
## education_level 1.182340e+01 7 1.192956
## years_education 1.069836e+01 1 3.270834
## marital_status 1.904703e+01 3 1.634198
## occupation 9.493631e+06 5 4.985896
## household_role 4.185563e+01 5 1.452698
## race 2.336804e+00 4 1.111931
## gender 2.797420e+00 1 1.672549
## hours_per_week 1.134154e+00 1 1.064966
## native_region 2.335193e+00 6 1.073232
## capital_gain_log 1.031773e+00 1 1.015762
## capital_loss_log 1.013270e+00 1 1.006613# Perform Hosmer-Lemeshow goodness of fit test
# Note: A p-value less than 0.05 typically suggests that the model does not fit the data well.
hoslem.test(training_set_out$income, fitted(model4))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: training_set_out$income, fitted(model4)
## X-squared = 26038, df = 8, p-value < 2.2e-16# hoslem.test(training_set_LR$income, fitted(model4)) # dummied variables - test comparison
# Make predictions
probabilities2 <- model4 %>% predict(testing_set_out, type = "response")
predicted.classes2 <- ifelse(probabilities2 > 0.275, ">50K", "<=50K")
# Model accuracy
accuracy2 <- mean(predicted.classes2==testing_set_out$income)
# confusion matrix
tb2 <- table(predicted.classes2, testing_set_out$income)
tb2##
## predicted.classes2 <=50K >50K
## <=50K 4018 296
## >50K 869 1326# Calculate Sensitivity (True Positive Rate)
sensitivity2 <- tb2[2, 2] / (tb2[2, 2] + tb2[1, 2])
# Calculate Specificity (True Negative Rate)
specificity2 <- tb2[1, 1] / (tb2[1, 1] + tb2[2, 1])
# Calculate Prevalence
prevalence2 <- (tb2[1, 2] + tb2[2, 2]) / sum(tb2)
# Calculate PPV (Positive Predictive Value)
ppv2 <- tb2[2, 2] / (tb2[2, 2] + tb2[2, 1])
# Calculate NPV (Negative Predictive Value)
npv2 <- tb2[1, 1] / (tb2[1, 1] + tb2[1, 2])
# Calculate AUROC
roc_obj2 <- roc(testing_set_out$income, probabilities2)## Setting levels: control = <=50K, case = >50K
## Setting direction: controls < casesauroc2 <- auc(roc_obj2)
# Print the metrics
cat("accuracy:", accuracy2, "\n")## accuracy: 0.8210171cat("Sensitivity:", sensitivity2, "\n")## Sensitivity: 0.8175092cat("Specificity:", specificity2, "\n")## Specificity: 0.8221813cat("Prevalence:", prevalence2, "\n")## Prevalence: 0.2491934cat("PPV:", ppv2, "\n")## PPV: 0.6041002cat("NPV:", npv2, "\n")## NPV: 0.9313862cat("AUROC:", auroc2, "\n")## AUROC: 0.9043508# full with outliers
pred_full <- prediction(probabilities, testing_set$income)
pred_full_per <- performance(pred_full, measure = "tpr", x.measure = "fpr")
mod1_pred <- data.frame(FP = pred_full_per@x.values[[1]], TP = pred_full_per@y.values[[1]])
# full without outliers
pred_full_Out <- prediction(probabilities2, testing_set_out$income)
pred_full_per2 <- performance(pred_full_Out, measure = "tpr", x.measure = "fpr")
mod2_pred <- data.frame(FP = pred_full_per2@x.values[[1]], TP = pred_full_per2@y.values[[1]])
# plot ROC curve for logistic regression
g <- ggplot() +
geom_line(data = mod1_pred, aes(x = FP, y = TP, color = 'Full With Outliers')) +
geom_line(data = mod2_pred, aes(x = FP, y = TP, color = 'Full Without Outliers')) +
geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1)) +
ggtitle('ROC Curve') +
labs(x = 'False Positive Rate', y = 'True Positive Rate')
g + scale_colour_manual(name = 'Classifier', values = c('Full With Outliers'='#E69F00',
'Full Without Outliers'='#56B4E9'))
######## Base test with all Categorical variables changed to Dummies/Continuous ##########
cat("\n\n----------------Variables changed to Continuous Dummies--------------\n\n")##
##
## ----------------Variables changed to Continuous Dummies--------------# Fit a logistic regression model
model5 <- glm(income ~ ., data = training_set_LR, family = binomial)
# Confidence interval
# confint(model3)
# Summarize the final selected model
summary(model5)##
## Call:
## glm(formula = income ~ ., family = binomial, data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.949965 0.275608 -14.332 < 2e-16 ***
## age 0.020252 0.001552 13.050 < 2e-16 ***
## workclass_num -0.034872 0.013227 -2.636 0.00838 **
## education_level_num 0.009517 0.007970 1.194 0.23240
## years_education 0.328684 0.010046 32.719 < 2e-16 ***
## marital_status_num -0.213962 0.033255 -6.434 1.24e-10 ***
## occupation_num -0.102148 0.017163 -5.952 2.65e-09 ***
## household_role_num -0.860918 0.022230 -38.727 < 2e-16 ***
## race_num -0.048769 0.031856 -1.531 0.12578
## gender_num -0.093611 0.057535 -1.627 0.10374
## hours_per_week 0.027728 0.001642 16.890 < 2e-16 ***
## native_region_num 0.016371 0.031068 0.527 0.59824
## capital_gain_log 0.199255 0.006884 28.943 < 2e-16 ***
## capital_loss_log 0.151495 0.009959 15.212 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 18580 on 26036 degrees of freedom
## AIC: 18608
##
## Number of Fisher Scoring iterations: 6# Check for multicollinearity
vif(model5) ## age workclass_num education_level_num years_education
## 1.063026 1.101114 1.506080 1.595790
## marital_status_num occupation_num household_role_num race_num
## 1.028023 1.201923 1.634901 1.059181
## gender_num hours_per_week native_region_num capital_gain_log
## 1.646428 1.071907 1.058778 1.019891
## capital_loss_log
## 1.008746# Perform Hosmer-Lemeshow goodness of fit test
# Note: A p-value less than 0.05 typically suggests that the model does not fit the data well.
#hoslem.test(training_set$income, fitted(model5))
# hoslem.test(training_set_LR$income, fitted(model4)) # dummied variables - test comparison
# Make predictions
probabilities <- model5 %>% predict(testing_set_LR, type = "response")
predicted.classes2 <- ifelse(probabilities > 0.27, "1", "0")
# Model accuracy
accuracy <- mean(predicted.classes2==testing_set_LR$income)
# confusion matrix
tb <- table(predicted.classes2, testing_set_LR$income)
tb##
## predicted.classes2 0 1
## 0 3937 299
## 1 991 1285# Calculate Sensitivity (True Positive Rate)
sensitivity <- tb[2, 2] / (tb[2, 2] + tb[1, 2])
# Calculate Specificity (True Negative Rate)
specificity <- tb[1, 1] / (tb[1, 1] + tb[2, 1])
# Calculate Prevalence
prevalence <- (tb[1, 2] + tb[2, 2]) / sum(tb)
# Calculate PPV (Positive Predictive Value)
ppv <- tb[2, 2] / (tb[2, 2] + tb[2, 1])
# Calculate NPV (Negative Predictive Value)
npv <- tb[1, 1] / (tb[1, 1] + tb[1, 2])
# Calculate AUROC
roc_obj <- roc(testing_set_LR$income, probabilities)## Setting levels: control = 0, case = 1
## Setting direction: controls < casesauroc <- auc(roc_obj)
# Print the metrics
cat("accuracy:", accuracy, "\n")## accuracy: 0.8019042cat("Sensitivity:", sensitivity, "\n")## Sensitivity: 0.8112374cat("Specificity:", specificity, "\n")## Specificity: 0.7989042cat("Prevalence:", prevalence, "\n")## Prevalence: 0.2432432cat("PPV:", ppv, "\n")## PPV: 0.564587cat("NPV:", npv, "\n")## NPV: 0.9294145cat("AUROC:", auroc, "\n")## AUROC: 0.8876979Odds ratio for the model removing influencial outliers.
An OR of x suggests that with each additional year of (the variable), the odds of the outcome occurring increase by approximately ((OR -1)*100) all other variables remaining constant. or if it has a reference variable…compared to the reference variable.
summary(model4)##
## Call:
## glm(formula = income ~ ., family = binomial, data = training_set_out)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -21.572497 254.007563 -0.085 0.932318
## age 0.025650 0.001782 14.398 < 2e-16 ***
## workclassFedGov 12.887309 254.007043 0.051 0.959536
## workclassLocGov 12.148596 254.007034 0.048 0.961853
## workclassNeverWorked -10.624428 290.331209 -0.037 0.970809
## workclassPrivate 12.327726 254.007025 0.049 0.961291
## workclassSelfEmpInc 12.534293 254.007040 0.049 0.960643
## workclassSelfEmpNotInc 11.863703 254.007032 0.047 0.962747
## workclassStateGov 12.048909 254.007042 0.047 0.962166
## education_levelhighsch -0.118089 0.067521 -1.749 0.080304 .
## education_levelmaster 0.221801 0.124468 1.782 0.074751 .
## education_levelno_edu -10.423748 115.347362 -0.090 0.927995
## education_levelphd 0.369327 0.218568 1.690 0.091075 .
## education_levelprimary 0.036119 0.344656 0.105 0.916536
## education_levelsecondary -0.387754 0.280117 -1.384 0.166280
## education_levelundergrad 0.077482 0.090763 0.854 0.393289
## years_education 0.253812 0.026904 9.434 < 2e-16 ***
## marital_statusmarried 0.781869 0.180984 4.320 1.56e-05 ***
## marital_statusnotmarried -0.455434 0.089140 -5.109 3.24e-07 ***
## marital_statuswidowed 0.201669 0.160488 1.257 0.208898
## occupationclerical 0.911643 0.151997 5.998 2.00e-09 ***
## occupationhighskill 1.502355 0.135967 11.049 < 2e-16 ***
## occupationlowskill 0.130073 0.158943 0.818 0.413152
## occupationmidskill 0.910502 0.137907 6.602 4.05e-11 ***
## occupationother 12.576305 254.007078 0.050 0.960512
## household_rolewife 1.290261 0.109616 11.771 < 2e-16 ***
## household_roletenant -0.830681 0.176768 -4.699 2.61e-06 ***
## household_rolesingle_occ -1.146927 0.198584 -5.776 7.67e-09 ***
## household_rolerelative -1.207740 0.233052 -5.182 2.19e-07 ***
## household_roledepchild -1.972228 0.222809 -8.852 < 2e-16 ***
## racewhite 0.591075 0.300803 1.965 0.049416 *
## raceblack 0.548002 0.309083 1.773 0.076230 .
## racenative -0.037869 0.385320 -0.098 0.921711
## raceAsian_Pac_Islander 0.845747 0.331458 2.552 0.010723 *
## gendermale 0.804968 0.082804 9.721 < 2e-16 ***
## hours_per_week 0.030229 0.001774 17.040 < 2e-16 ***
## native_regionEAsia 0.175254 0.230713 0.760 0.447483
## native_regionEurope 0.543644 0.186354 2.917 0.003531 **
## native_regionNorthAmerica 0.460419 0.122350 3.763 0.000168 ***
## native_regionSAsia -0.239784 0.288012 -0.833 0.405100
## native_regionSouthAmerica -0.701492 0.331371 -2.117 0.034265 *
## native_regionUSTerritory -0.239327 0.432480 -0.553 0.580002
## capital_gain_log 0.208900 0.007213 28.961 < 2e-16 ***
## capital_loss_log 0.144352 0.010457 13.804 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28594 on 26037 degrees of freedom
## Residual deviance: 17146 on 25994 degrees of freedom
## AIC: 17234
##
## Number of Fisher Scoring iterations: 13# Calculate Odds Ratios and 90% Confidence Intervals
odds_ratios <- exp(coef(model4))
conf_int <- exp(confint(model4, level = 0.95))## Waiting for profiling to be done...# Identify significant predictors at the 0.05 level
significant_predictors <- summary(model4)$coefficients[, 4] < 0.05
# Extract Odds Ratios and Confidence Intervals for significant predictors
# Note: Use names() to match significant predictors by name
significant_or_ci <- data.frame(
OddsRatio = odds_ratios[significant_predictors],
LowerCI = conf_int[names(odds_ratios)[significant_predictors], 1],
UpperCI = conf_int[names(odds_ratios)[significant_predictors], 2]
)
significant_or_ci## OddsRatio LowerCI UpperCI
## age 1.0259819 1.02241006 1.0295756
## years_education 1.2889299 1.22321054 1.3592773
## marital_statusmarried 2.1855539 1.52215528 3.0966740
## marital_statusnotmarried 0.6341729 0.53249204 0.7552620
## occupationclerical 2.4884069 1.85387637 3.3651739
## occupationhighskill 4.4922577 3.45714650 5.8934846
## occupationmidskill 2.4855707 1.90549327 3.2731010
## household_rolewife 3.6337365 2.93261839 4.5070367
## household_roletenant 0.4357524 0.30589595 0.6121163
## household_rolesingle_occ 0.3176113 0.21370898 0.4657357
## household_rolerelative 0.2988719 0.18616853 0.4651031
## household_roledepchild 0.1391465 0.08895833 0.2132226
## racewhite 1.8059290 1.02752536 3.3547799
## raceAsian_Pac_Islander 2.3297182 1.24155509 4.5706561
## gendermale 2.2366252 1.90280305 2.6326549
## hours_per_week 1.0306901 1.02712050 1.0342885
## native_regionEurope 1.7222709 1.19489707 2.4813807
## native_regionNorthAmerica 1.5847386 1.24956523 2.0189561
## native_regionSouthAmerica 0.4958451 0.25477575 0.9363712
## capital_gain_log 1.2323223 1.21510591 1.2499571
## capital_loss_log 1.1552901 1.13189335 1.1792630Interpretation:
age: An odds ratio (OR) of 1.02560964 means that with each additional year of age, the odds of the outcome occurring increase by approximately 2.56%. The calculation is ((OR - 1) = (1.02560964 - 1) = 2.56%). similar_pop_count: An OR of 1.00000063 indicates an extremely small increase in odds with each unit increase in similar population count. The effect is so small it’s practically negligible. years_education: An OR of 1.27846414 suggests that with each additional year of education, the odds of the outcome occurring increase by 27.85%. The calculation is ((1.27846414 - 1) = 27.85%). marital_statusmarried: An OR of 1.83198084 means that being married is associated with an 83.20% increase in the odds of the outcome occurring compared to not being married. marital_statusnotmarried: An OR of 0.55312175 indicates that not being married is associated with a 44.69% decrease in the odds of the outcome occurring compared to being married. The calculation is ((1 - 0.55312175) = 44.69%). occupationclerical: An OR of 2.73417509 suggests that being in a clerical occupation is associated with a 173.42% increase in the odds of the outcome occurring compared to not being in this occupation. occupationhighskill: An OR of 4.87181987 indicates that being in a high-skill occupation is associated with a 387.18% increase in the odds of the outcome occurring compared to not being in this occupation. occupationmidskill: An OR of 2.61052015 means that being in a mid-skill occupation is associated with a 161.05% increase in the odds of the outcome occurring compared to not being in this occupation. household_rolewife: An OR of 4.11794882 suggests that being the wife in a household is associated with a 311.79% increase in the odds of the outcome occurring compared to not being the wife. household_roletenant: An OR of 0.34585959 indicates that being a tenant is associated with a 65.41% decrease in the odds of the outcome occurring compared to not being a tenant. household_rolesingle_occ: An OR of 0.29151166 means that being a single occupant is associated with a 70.85% decrease in the odds of the outcome occurring compared to not being a single occupant. household_rolerelative: An OR of 0.23532074 suggests that being a relative in a household is associated with a 76.47% decrease in the odds of the outcome occurring compared to not being a relative. household_roledepchild: An OR of 0.11687608 indicates that being a dependent child is associated with an 88.31% decrease in the odds of the outcome occurring compared to not being a dependent child. gendermale: An OR of 2.45071926 means that being male is associated with a 145.07% increase in the odds of the outcome occurring compared to not being male. capital_gain: An OR of 1.00097874 suggests a very slight increase in the odds of the outcome occurring with each unit increase in capital gain. capital_loss: An OR of 1.00246863 indicates a slight increase in the odds of the outcome occurring with each unit increase in capital loss. hours_per_week: An OR of 1.02929197 means that with each additional hour worked per week, the odds of the outcome occurring increase by approximately 2.93%. native_countrySouthAmerica: An OR of 0.19791245 suggests that being from South America is associated with an 80.21% decrease in the odds of the outcome occurring compared to not being from South America. investedInvested: An OR of 0.02628912 indicates that having invested is associated with a 97.37% decrease in the odds of the outcome occurring compared to not having invested.
Drop for code or run testing_set as well:
Build a reduced model from the significant variables
- Test cost for automating extracting the reduced model.
- The testing set would also need to be refined for this formula.
# Get the original variable names (excluding the response variable)
original_vars <- setdiff(names(training_set), "income")
# Summary of the full model
summary_full <- summary(model3)
# Extracting significant variables
significant_vars <- summary_full$coefficients[summary_full$coefficients[, "Pr(>|z|)"] < 0.05, ]
significant_vars_names <- rownames(significant_vars)
# Initialize an empty string to build the new formula
new_formula <- "income ~ "
# Create a list to keep track of which categorical variables have all non-reference levels significant
all_levels_significant <- list()
# Loop through each original variable to check if all non-reference levels are significant
for(original_var in original_vars) {
# Get all non-reference levels of the categorical variable
levels <- levels(training_set[[original_var]])[-1] # Exclude the first level which is the reference
# Check if all non-reference levels are significant
all_significant <- all(sapply(levels, function(lvl) paste0(original_var, lvl) %in% significant_vars_names))
# Store the result
all_levels_significant[[original_var]] <- all_significant
}
# Loop through each significant variable
for(var in significant_vars_names) {
# Initialize new_var_expr
new_var_expr <- var
# Check if var is a level of an original variable
for(original_var in original_vars) {
if(grepl(original_var, var)) {
# Check if all non-reference levels of the variable are significant
if(all_levels_significant[[original_var]]) {
# If all non-reference levels are significant, use the original variable name
new_var_expr <- original_var
} else {
# If not all levels are significant, construct the new variable expression
level <- gsub(paste0("^", original_var), "", var)
if(nchar(level) > 0) {
new_var_expr <- paste0("(training_set$", original_var, " == '", level, "')")
}
}
# Ensure only the condition is added to the formula, not the logical result
new_formula <- paste(new_formula, new_var_expr, sep = " + ")
break
}
}
}
# Remove the trailing " + " if present
new_formula <- sub(" \\+ $", "", new_formula)
# Convert the string to a formula
new_formula <- as.formula(new_formula)
# Fit the reduced model
reduced_model <- glm(new_formula, data = training_set, family = binomial)
summary(reduced_model)##
## Call:
## glm(formula = new_formula, family = binomial, data = training_set)
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -8.592742 0.276551 -31.071
## age 0.024959 0.001698 14.696
## years_education 0.291422 0.009366 31.115
## training_set$marital_status == "married"TRUE 0.578213 0.179589 3.220
## training_set$marital_status == "notmarried"TRUE -0.545503 0.086698 -6.292
## training_set$occupation == "clerical"TRUE 0.956324 0.090291 10.592
## training_set$occupation == "highskill"TRUE 1.429749 0.067099 21.308
## training_set$occupation == "midskill"TRUE 0.850969 0.071166 11.958
## household_rolewife 1.273986 0.108689 11.721
## household_roletenant -0.969333 0.177348 -5.466
## household_rolesingle_occ -1.183018 0.197787 -5.981
## household_rolerelative -1.527496 0.255483 -5.979
## household_roledepchild -1.981139 0.218008 -9.087
## gendermale 0.801519 0.081839 9.794
## hours_per_week 0.029168 0.001702 17.136
## training_set$native_region == "Europe"TRUE 0.301314 0.162038 1.860
## training_set$native_region == "NorthAmerica"TRUE 0.280651 0.087359 3.213
## training_set$native_region == "SouthAmerica"TRUE -0.829061 0.324784 -2.553
## capital_gain_log 0.209875 0.007195 29.170
## capital_loss_log 0.155292 0.010487 14.808
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## age < 2e-16 ***
## years_education < 2e-16 ***
## training_set$marital_status == "married"TRUE 0.00128 **
## training_set$marital_status == "notmarried"TRUE 3.13e-10 ***
## training_set$occupation == "clerical"TRUE < 2e-16 ***
## training_set$occupation == "highskill"TRUE < 2e-16 ***
## training_set$occupation == "midskill"TRUE < 2e-16 ***
## household_rolewife < 2e-16 ***
## household_roletenant 4.61e-08 ***
## household_rolesingle_occ 2.21e-09 ***
## household_rolerelative 2.25e-09 ***
## household_roledepchild < 2e-16 ***
## gendermale < 2e-16 ***
## hours_per_week < 2e-16 ***
## training_set$native_region == "Europe"TRUE 0.06295 .
## training_set$native_region == "NorthAmerica"TRUE 0.00132 **
## training_set$native_region == "SouthAmerica"TRUE 0.01069 *
## capital_gain_log < 2e-16 ***
## capital_loss_log < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28679 on 26049 degrees of freedom
## Residual deviance: 17282 on 26030 degrees of freedom
## AIC: 17322
##
## Number of Fisher Scoring iterations: 7# Check for multicollinearity
vif(reduced_model) ## GVIF Df GVIF^(1/(2*Df))
## age 1.149736 1 1.072258
## years_education 1.288334 1 1.135048
## training_set$marital_status == "married" 15.822863 1 3.977796
## training_set$marital_status == "notmarried" 1.962686 1 1.400959
## training_set$occupation == "clerical" 1.720833 1 1.311805
## training_set$occupation == "highskill" 3.066719 1 1.751205
## training_set$occupation == "midskill" 2.732219 1 1.652943
## household_role 41.375352 5 1.451023
## gender 2.734712 1 1.653697
## hours_per_week 1.075219 1 1.036928
## training_set$native_region == "Europe" 1.352421 1 1.162936
## training_set$native_region == "NorthAmerica" 1.437131 1 1.198804
## training_set$native_region == "SouthAmerica" 1.069315 1 1.034077
## capital_gain_log 1.028121 1 1.013963
## capital_loss_log 1.011196 1 1.005582# Perform Hosmer-Lemeshow goodness of fit test
# Note: A p-value less than 0.05 typically suggests that the model does not fit the data well.
hoslem.test(training_set$income, fitted(reduced_model))## Warning in Ops.factor(1, y): '-' not meaningful for factors##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: training_set$income, fitted(reduced_model)
## X-squared = 26050, df = 8, p-value < 2.2e-16# hoslem.test(training_set_LR$income, fitted(model4)) # dummied variables - test comparisonPCA Sources: https://www.analyticsvidhya.com/blog/2021/09/pca-and-its-underlying-mathematical-principles/ # PCA full model Test: For PC1: marital_status_num and education_level have high positive loading scores, while age has a high negative loading score. These variables significantly influence PC1. For PC2: capital_gain and capital_loss are notable due to their high positive loading scores, making them important for PC2. For PC3: hours_per_week stands out with a high positive loading score, suggesting its relevance to PC3. 12 variables are needed for 90 percent.
pc.result<-prcomp(training_set_LR[,1:14],scale.=TRUE)
#Eigen Vectors
pc.result$rotation## PC1 PC2 PC3 PC4
## age 0.31792769 -0.24271042 -0.085668117 -0.037919086
## workclass_num 0.14095051 0.23644137 0.482221275 -0.456920690
## education_level_num 0.25400994 0.36542502 -0.277883643 0.170273682
## years_education 0.28829059 0.46773963 -0.153503945 0.217183126
## marital_status_num -0.26385340 0.24020729 -0.106018694 0.218002885
## occupation_num -0.12087948 -0.53561997 -0.300610524 0.220512146
## household_role_num -0.46755145 0.28058855 -0.009975435 0.077229592
## race_num -0.05408547 0.08573615 -0.499432505 -0.463637835
## gender_num 0.27720723 -0.30470870 0.034917009 -0.046648163
## hours_per_week 0.31364444 -0.01780335 0.166271112 -0.193349505
## native_region_num 0.01103511 -0.04492601 0.492497616 0.506198227
## income 0.43606568 0.06406484 -0.124912682 0.176440226
## capital_gain_log 0.21937696 0.04870274 -0.141901146 0.263701201
## capital_loss_log 0.10974690 0.02331202 -0.032304082 -0.009081218
## PC5 PC6 PC7 PC8
## age 0.467926501 0.19904330 0.008743525 -0.33759508
## workclass_num -0.057179661 -0.16158067 -0.175456881 -0.29127317
## education_level_num -0.100478701 0.06709900 0.407871213 -0.27150733
## years_education -0.043144756 0.08381939 0.201809153 0.12521691
## marital_status_num -0.415955578 -0.20004158 -0.212501926 -0.34323098
## occupation_num -0.215156801 -0.03783706 0.079168681 0.02767957
## household_role_num -0.009597195 -0.02473937 -0.072474111 0.15434364
## race_num 0.038442710 -0.04421340 -0.072307320 0.33419466
## gender_num -0.537315285 -0.24128658 0.056132550 -0.20951621
## hours_per_week -0.299002720 -0.13009116 0.179495422 0.56154820
## native_region_num 0.063144843 0.09736785 0.066991065 0.27790315
## income -0.038539884 -0.02775040 -0.268041166 0.07445766
## capital_gain_log 0.242066306 -0.51865744 -0.554725239 0.11503255
## capital_loss_log -0.320557895 0.72410724 -0.529833511 0.04972216
## PC9 PC10 PC11 PC12
## age -0.1712540233 -0.44290764 -0.14249360 0.005664407
## workclass_num -0.1406105906 0.24246564 -0.20944436 0.429952126
## education_level_num 0.0004516579 0.10841238 -0.53072802 0.038463589
## years_education -0.0362201267 0.16926544 0.26424179 -0.009721650
## marital_status_num -0.2180700054 -0.59003152 0.05301775 0.110022992
## occupation_num 0.0132297116 0.21750395 -0.24094836 0.560220529
## household_role_num 0.1018832781 0.08066802 -0.14738982 0.033603051
## race_num -0.6312142160 0.02357549 -0.05060070 0.001417756
## gender_num -0.2086743415 0.21389541 0.09621515 -0.459243201
## hours_per_week 0.2434027022 -0.49122924 -0.25534522 0.086943289
## native_region_num -0.6120214164 0.01875721 -0.14627861 0.019668522
## income -0.0006707552 0.01110523 0.47137321 0.460398624
## capital_gain_log 0.1097644222 0.10691675 -0.35914032 -0.205106635
## capital_loss_log 0.0603218854 0.05627741 -0.22836605 -0.116472840
## PC13 PC14
## age 0.24769975 -0.389139603
## workclass_num 0.17418497 -0.016865719
## education_level_num -0.38215608 0.010452400
## years_education 0.67984989 0.002459302
## marital_status_num 0.09336407 0.136351975
## occupation_num 0.29204299 0.008597852
## household_role_num 0.01979726 -0.791638051
## race_num -0.05280795 0.025412808
## gender_num 0.02538533 -0.350480187
## hours_per_week 0.05495421 -0.077893820
## native_region_num -0.08353689 0.014101759
## income -0.42524392 -0.253469728
## capital_gain_log 0.09554074 0.087266049
## capital_loss_log 0.04770228 0.034688360#Eigen Values
cat("\n\n----------------Eigen Values--------------\n\n")##
##
## ----------------Eigen Values--------------eigenvals<-pc.result$sdev^2
eigenvals## [1] 2.5788810 1.5795207 1.2462407 1.1585182 1.0925006 1.0534715 0.8975982
## [8] 0.8427692 0.7900178 0.6649168 0.6530519 0.5406276 0.5000947 0.4017913# Calculate cumulative proportion of variance explained
cumulative_var <- cumsum(eigenvals) / sum(eigenvals)
# Find the number of PCs needed to retain 90% of the variance
num_pcs <- which(cumulative_var >= 0.9)[1]
# number of PCs
num_pcs## [1] 12#Scree plot
par(mfrow=c(1,2))
plot(eigenvals,type="l",main="Scree Plot",ylab="Eigen Values",xlab="PC #")
plot(eigenvals/sum(eigenvals),type="l",main="Explained Variance",ylab = "Cumulative Proportion", xlab = "Principal Component", ylim=c(0,1))
cumulative.prop<-cumsum(eigenvals/sum(eigenvals))
lines(cumulative.prop,type = "o", lty=2)
abline(h = 0.9, col = "red", lty = 2) # Add a line at 90% variance explained
legend("topright", legend=c("Prop","Cumulative"),
lty=1:2, cex=0.8)
PCA reduced Model: PC1: household_role_num (0.49898102), invested_num (-0.38056203), age (-0.36436500) PC2: invested_num (0.5153051), capital_loss (0.4491392), years_education (0.2559078) PC3: occupation_num (0.63406086), capital_loss (0.36206738), invested_num (0.28760465)
pc.result <- prcomp(training_set_LR[, c("age", "years_education", "capital_gain_log", "capital_loss_log",
"marital_status_num", "occupation_num", "household_role_num", "gender_num",
"hours_per_week")], scale. = TRUE)
#Eigen Vectors
pc.result$rotation## PC1 PC2 PC3 PC4 PC5
## age 0.41836270 -0.07709328 0.426940935 -0.30567639 -0.2012083
## years_education 0.18626005 0.60541833 -0.144869887 0.09823078 -0.1333000
## capital_gain_log 0.20422358 0.17537627 0.354677374 0.52931255 -0.5361262
## capital_loss_log 0.10368952 0.07191339 -0.515431855 -0.56465581 -0.5404645
## marital_status_num -0.37864274 0.04269248 -0.302994505 0.34113460 -0.3645320
## occupation_num -0.02495235 -0.67953039 -0.042899894 0.10467422 -0.2040208
## household_role_num -0.57235231 0.10998451 -0.001498099 0.02409842 0.0205287
## gender_num 0.36266056 -0.31073858 -0.420182791 0.35537690 -0.0412253
## hours_per_week 0.36703986 0.13913177 -0.367513570 0.20904956 0.4307948
## PC6 PC7 PC8 PC9
## age 0.3050613 0.26440545 0.38434985 0.438326840
## years_education 0.2800481 -0.64842898 0.20777550 0.091173745
## capital_gain_log -0.4563607 0.09310870 -0.13074328 -0.010424847
## capital_loss_log -0.3081206 0.08087859 -0.07706553 0.007086762
## marital_status_num 0.4079040 0.40887436 0.39873647 -0.145568702
## occupation_num -0.1732588 -0.51335807 0.43472751 -0.025517083
## household_role_num -0.2475375 -0.05116943 0.02893196 0.771091217
## gender_num 0.2995234 0.01350624 -0.45150464 0.416677198
## hours_per_week -0.4217985 0.24678649 0.48311422 0.096721812#Eigen Values
cat("\n\n----------------Eigen Values--------------\n\n")##
##
## ----------------Eigen Values--------------eigenvals<-pc.result$sdev^2
eigenvals## [1] 2.0923400 1.3442676 1.0788525 1.0528684 0.9107656 0.7782152 0.6995701
## [8] 0.6272387 0.4158820# Calculate cumulative proportion of variance explained
cumulative_var <- cumsum(eigenvals) / sum(eigenvals)
# Find the number of PCs needed to retain 90% of the variance
num_pcs <- which(cumulative_var >= 0.9)[1]
# number of PCs
num_pcs## [1] 8#Scree plot
par(mfrow=c(1,2))
plot(eigenvals,type="l",main="Scree Plot",ylab="Eigen Values",xlab="PC #")
plot(eigenvals/sum(eigenvals),type="l",main="Explained Variance",ylab = "Cumulative Proportion", xlab = "Principal Component", ylim=c(0,1))
cumulative.prop<-cumsum(eigenvals/sum(eigenvals))
lines(cumulative.prop,type = "o", lty=2)
abline(h = 0.9, col = "red", lty = 2) # Add a line at 90% variance explained
legend("topright", legend=c("Prop","Cumulative"),
lty=1:2, cex=0.8)
GLMNET Feature Test - one variable was dropped.
library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## lift# change the income value names to work with glmnet (greater than sign will not run)
training_set1 <- training_set
training_set1$income <- gsub('<=50K', 'less_than_50K', training_set1$income)
training_set1$income <- gsub('>50K', 'greater_than_50K', training_set1$income)
# Convert 'income' to a factor if it's not already
training_set1$income <- as.factor(training_set1$income)
levels(training_set1$income)## [1] "greater_than_50K" "less_than_50K"# Ensure that train_control is set up for classification
train_control <- trainControl(method = "cv", number = 10, classProbs = TRUE, summaryFunction = twoClassSummary)
# Now, re-run the GLMNET model training
set.seed(1234)
glmnet_model <- train(
income ~ .,
data = training_set1, # Exclude the 'id_number' column
method = "glmnet",
trControl = train_control,
preProcess = c("center", "scale"), # Standardize predictors
tuneLength = 10,
metric = "ROC" # Use AUROC curve error metric
)
# Comment on which predictors were left or dropped
coef(glmnet_model$finalModel, glmnet_model$bestTune$lambda)## 44 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 2.1641367522
## age -0.3422553887
## workclassFedGov -0.0880720200
## workclassLocGov 0.0404181528
## workclassNeverWorked 0.0123280280
## workclassPrivate -0.0043169916
## workclassSelfEmpInc -0.0468429871
## workclassSelfEmpNotInc 0.1276348832
## workclassStateGov 0.0416790959
## education_levelhighsch 0.0640201413
## education_levelmaster -0.0369197731
## education_levelno_edu 0.0548526913
## education_levelphd -0.0226507466
## education_levelprimary .
## education_levelsecondary 0.0612676740
## education_levelundergrad -0.0267679085
## years_education -0.6447565368
## marital_statusmarried -0.3859331574
## marital_statusnotmarried 0.2550689853
## marital_statuswidowed -0.0075526885
## occupationclerical -0.2679247256
## occupationhighskill -0.6898580980
## occupationlowskill -0.0002064879
## occupationmidskill -0.3532885251
## occupationother -0.0370003551
## household_rolewife -0.2759919048
## household_roletenant 0.3390424736
## household_rolesingle_occ 0.3015901303
## household_rolerelative 0.2266473525
## household_roledepchild 0.6330061574
## racewhite -0.0172032700
## raceblack -0.0002490096
## racenative 0.0342695053
## raceAsian_Pac_Islander -0.0176469615
## gendermale -0.3756751899
## hours_per_week -0.3660032658
## native_regionEAsia -0.0436932168
## native_regionEurope -0.0530264914
## native_regionNorthAmerica -0.1128296577
## native_regionSAsia 0.0035046285
## native_regionSouthAmerica 0.0529599638
## native_regionUSTerritory 0.0111481535
## capital_gain_log -0.5150618328
## capital_loss_log -0.2431100536GLMNET Feature Test reduced_ - one variable was dropped.
library(caret)
# change the income value names to work with glmnet (greater than sign will not run)
training_set1 <- training_set_out
training_set1$income <- gsub('<=50K', 'less_than_50K', training_set1$income)
training_set1$income <- gsub('>50K', 'greater_than_50K', training_set1$income)
# Convert 'income' to a factor if it's not already
training_set1$income <- as.factor(training_set1$income)
levels(training_set1$income)## [1] "greater_than_50K" "less_than_50K"# Ensure that train_control is set up for classification
train_control <- trainControl(method = "cv", number = 10, classProbs = TRUE, summaryFunction = twoClassSummary)
# Now, re-run the GLMNET model training
set.seed(1234)
glmnet_model <- train(
income ~ .,
data = training_set1, # Exclude the 'id_number' column
method = "glmnet",
trControl = train_control,
preProcess = c("center", "scale"), # Standardize predictors
tuneLength = 10,
metric = "ROC" # Use AUROC curve error metric
)
# Comment on which predictors were left or dropped
coef(glmnet_model$finalModel, glmnet_model$bestTune$lambda)## 44 x 1 sparse Matrix of class "dgCMatrix"
## s1
## (Intercept) 2.184064080
## age -0.347441900
## workclassFedGov -0.109627998
## workclassLocGov 0.020797011
## workclassNeverWorked 0.030800314
## workclassPrivate -0.041526626
## workclassSelfEmpInc -0.053836957
## workclassSelfEmpNotInc 0.102729809
## workclassStateGov 0.036123548
## education_levelhighsch 0.064365676
## education_levelmaster -0.055377282
## education_levelno_edu 0.096875513
## education_levelphd -0.046338046
## education_levelprimary 0.002533377
## education_levelsecondary 0.057945695
## education_levelundergrad -0.037033422
## years_education -0.631805846
## marital_statusmarried -0.442984394
## marital_statusnotmarried 0.216503816
## marital_statuswidowed -0.032451839
## occupationclerical -0.243704988
## occupationhighskill -0.668698245
## occupationlowskill 0.004878430
## occupationmidskill -0.326242509
## occupationother -0.044433144
## household_rolewife -0.273632402
## household_roletenant 0.312731535
## household_rolesingle_occ 0.315596833
## household_rolerelative 0.191799544
## household_roledepchild 0.656262762
## racewhite -0.145170591
## raceblack -0.107275376
## racenative 0.020114050
## raceAsian_Pac_Islander -0.115152945
## gendermale -0.375324679
## hours_per_week -0.364851553
## native_regionEAsia -0.020730825
## native_regionEurope -0.065321317
## native_regionNorthAmerica -0.121991460
## native_regionSAsia 0.016934639
## native_regionSouthAmerica 0.053613873
## native_regionUSTerritory 0.016846083
## capital_gain_log -0.510132917
## capital_loss_log -0.227234084Stepwise feature selection using AIC for the step evaluation
- Sources: http://www.sthda.com/english/articles/36-classification-methods-essentials/150-stepwise-logistic-regression-essentials-in-r/
- Note: When comparing models, the one with the lower AIC is generally preferred because it suggests a better fit with fewer parameters.
- To compare models of different sizes using deviance, you need to adjust for the number of predictors, which is what AIC does by penalizing more complex models2.
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectcat("\n\n--------modelFull--------\n\n") # same as model 3 (46 total variables)##
##
## --------modelFull--------full.model <- glm(income ~ ., data = training_set, family = binomial)
coef(full.model)## (Intercept) age workclassFedGov
## -21.89895439 0.02533892 13.55632010
## workclassLocGov workclassNeverWorked workclassPrivate
## 12.85409218 -10.62424357 13.03852616
## workclassSelfEmpInc workclassSelfEmpNotInc workclassStateGov
## 13.29226613 12.56797972 12.80217183
## education_levelhighsch education_levelmaster education_levelno_edu
## -0.12944160 0.16680035 -10.21910205
## education_levelphd education_levelprimary education_levelsecondary
## 0.20818140 0.06426630 -0.45394455
## education_levelundergrad years_education marital_statusmarried
## 0.06948486 0.25241768 0.60321635
## marital_statusnotmarried marital_statuswidowed occupationclerical
## -0.54348582 0.06997208 1.08992395
## occupationhighskill occupationlowskill occupationmidskill
## 1.63680520 0.25306813 1.07019874
## occupationother household_rolewife household_roletenant
## 13.43787005 1.30301005 -0.95623780
## household_rolesingle_occ household_rolerelative household_roledepchild
## -1.16493905 -1.50598489 -1.96855753
## racewhite raceblack racenative
## 0.28214579 0.24280250 -0.14146165
## raceAsian_Pac_Islander gendermale hours_per_week
## 0.34776118 0.80951822 0.03039225
## native_regionEAsia native_regionEurope native_regionNorthAmerica
## 0.37625231 0.44993221 0.43822860
## native_regionSAsia native_regionSouthAmerica native_regionUSTerritory
## -0.05089006 -0.70621248 -0.15850731
## capital_gain_log capital_loss_log
## 0.21143341 0.15542604# Make predictions
probabilities <- full.model %>% predict(testing_set, type = "response")
predicted.classes <- ifelse(probabilities > 0.24, ">50K", "<=50K")
# Prediction accuracy
observed.classes <- testing_set$income
mean(predicted.classes == observed.classes)## [1] 0.7982187summary(full.model)##
## Call:
## glm(formula = income ~ ., family = binomial, data = training_set)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -21.898954 225.382927 -0.097 0.922597
## age 0.025339 0.001783 14.209 < 2e-16 ***
## workclassFedGov 13.556320 225.382382 0.060 0.952038
## workclassLocGov 12.854092 225.382372 0.057 0.954519
## workclassNeverWorked -10.624244 288.906847 -0.037 0.970665
## workclassPrivate 13.038526 225.382363 0.058 0.953868
## workclassSelfEmpInc 13.292266 225.382379 0.059 0.952971
## workclassSelfEmpNotInc 12.567980 225.382370 0.056 0.955531
## workclassStateGov 12.802172 225.382381 0.057 0.954703
## education_levelhighsch -0.129442 0.067633 -1.914 0.055635 .
## education_levelmaster 0.166800 0.124686 1.338 0.180973
## education_levelno_edu -10.219102 115.190388 -0.089 0.929309
## education_levelphd 0.208181 0.220107 0.946 0.344241
## education_levelprimary 0.064266 0.344841 0.186 0.852158
## education_levelsecondary -0.453945 0.278697 -1.629 0.103353
## education_levelundergrad 0.069485 0.090666 0.766 0.443448
## years_education 0.252418 0.026951 9.366 < 2e-16 ***
## marital_statusmarried 0.603216 0.181376 3.326 0.000882 ***
## marital_statusnotmarried -0.543486 0.088941 -6.111 9.93e-10 ***
## marital_statuswidowed 0.069972 0.162197 0.431 0.666176
## occupationclerical 1.089924 0.155985 6.987 2.80e-12 ***
## occupationhighskill 1.636805 0.140002 11.691 < 2e-16 ***
## occupationlowskill 0.253068 0.163050 1.552 0.120640
## occupationmidskill 1.070199 0.141919 7.541 4.67e-14 ***
## occupationother 13.437870 225.382424 0.060 0.952456
## household_rolewife 1.303010 0.109743 11.873 < 2e-16 ***
## household_roletenant -0.956238 0.178110 -5.369 7.93e-08 ***
## household_rolesingle_occ -1.164939 0.198471 -5.870 4.37e-09 ***
## household_rolerelative -1.505985 0.255536 -5.893 3.78e-09 ***
## household_roledepchild -1.968558 0.219054 -8.987 < 2e-16 ***
## racewhite 0.282146 0.280508 1.006 0.314493
## raceblack 0.242802 0.289683 0.838 0.401937
## racenative -0.141462 0.366415 -0.386 0.699445
## raceAsian_Pac_Islander 0.347761 0.314142 1.107 0.268285
## gendermale 0.809518 0.082954 9.759 < 2e-16 ***
## hours_per_week 0.030392 0.001767 17.204 < 2e-16 ***
## native_regionEAsia 0.376252 0.229222 1.641 0.100709
## native_regionEurope 0.449932 0.183403 2.453 0.014158 *
## native_regionNorthAmerica 0.438229 0.122056 3.590 0.000330 ***
## native_regionSAsia -0.050890 0.304402 -0.167 0.867228
## native_regionSouthAmerica -0.706212 0.347370 -2.033 0.042050 *
## native_regionUSTerritory -0.158507 0.416619 -0.380 0.703603
## capital_gain_log 0.211433 0.007256 29.141 < 2e-16 ***
## capital_loss_log 0.155426 0.010570 14.704 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28679 on 26049 degrees of freedom
## Residual deviance: 17139 on 26006 degrees of freedom
## AIC: 17227
##
## Number of Fisher Scoring iterations: 13# confusion matrix
tb <- table(predicted.classes, testing_set$income)
tb##
## predicted.classes <=50K >50K
## <=50K 3865 270
## >50K 1044 1333# Model accuracy
accuracy <- mean(predicted.classes==testing_set$income)
# Calculate Sensitivity (True Positive Rate)
sensitivity <- tb[2, 2] / (tb[2, 2] + tb[1, 2])
# Calculate Specificity (True Negative Rate)
specificity <- tb[1, 1] / (tb[1, 1] + tb[2, 1])
# Calculate Prevalence
prevalence <- (tb[1, 2] + tb[2, 2]) / sum(tb)
# Calculate PPV (Positive Predictive Value)
ppv <- tb[2, 2] / (tb[2, 2] + tb[2, 1])
# Calculate NPV (Negative Predictive Value)
npv <- tb[1, 1] / (tb[1, 1] + tb[1, 2])
# Calculate AUROC
roc_obj <- roc(testing_set$income, probabilities)## Setting levels: control = <=50K, case = >50K## Setting direction: controls < casesauroc <- auc(roc_obj)
# Print the metrics
cat("accuracy:", accuracy, "\n")## accuracy: 0.7982187cat("Sensitivity:", sensitivity, "\n")## Sensitivity: 0.8315658cat("Specificity:", specificity, "\n")## Specificity: 0.7873294cat("Prevalence:", prevalence, "\n")## Prevalence: 0.2461609cat("PPV:", ppv, "\n")## PPV: 0.5607909cat("NPV:", npv, "\n")## NPV: 0.9347037cat("AUROC:", auroc, "\n")## AUROC: 0.8945609cat("\n\n--------modelAICStep--------\n\n") # (35 variables recommended)##
##
## --------modelAICStep--------step.model <- full.model %>% stepAIC(trace = FALSE)
coef(step.model)## (Intercept) age workclassFedGov
## -21.08434284 0.02554287 12.56160190
## workclassLocGov workclassNeverWorked workclassPrivate
## 11.86640918 -9.67439650 12.04888714
## workclassSelfEmpInc workclassSelfEmpNotInc workclassStateGov
## 12.30433796 11.57160446 11.81469985
## years_education marital_statusmarried marital_statusnotmarried
## 0.29291556 0.60031961 -0.54040426
## marital_statuswidowed occupationclerical occupationhighskill
## 0.06511037 1.08509161 1.63834935
## occupationlowskill occupationmidskill occupationother
## 0.24658293 1.06031774 12.43947472
## household_rolewife household_roletenant household_rolesingle_occ
## 1.29666739 -0.96119615 -1.17144784
## household_rolerelative household_roledepchild gendermale
## -1.51606155 -1.97178053 0.80968710
## hours_per_week native_regionEAsia native_regionEurope
## 0.03039783 0.43859907 0.45479671
## native_regionNorthAmerica native_regionSAsia native_regionSouthAmerica
## 0.43525709 -0.02262065 -0.68657397
## native_regionUSTerritory capital_gain_log capital_loss_log
## -0.20566146 0.21103604 0.15559172# Make predictions
probabilities <- predict(step.model, testing_set, type = "response")
predicted.classes <- ifelse(probabilities > 0.24, ">50K", "<=50K")
# confusion matrix
tb <- table(predicted.classes, testing_set$income)
tb##
## predicted.classes <=50K >50K
## <=50K 3856 267
## >50K 1053 1336summary(step.model)##
## Call:
## glm(formula = income ~ age + workclass + years_education + marital_status +
## occupation + household_role + gender + hours_per_week + native_region +
## capital_gain_log + capital_loss_log, family = binomial, data = training_set)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -21.084343 136.264936 -0.155 0.877034
## age 0.025543 0.001765 14.475 < 2e-16 ***
## workclassFedGov 12.561602 136.264582 0.092 0.926551
## workclassLocGov 11.866409 136.264565 0.087 0.930605
## workclassNeverWorked -9.674397 174.414262 -0.055 0.955766
## workclassPrivate 12.048887 136.264549 0.088 0.929541
## workclassSelfEmpInc 12.304338 136.264577 0.090 0.928051
## workclassSelfEmpNotInc 11.571604 136.264562 0.085 0.932325
## workclassStateGov 11.814700 136.264580 0.087 0.930907
## years_education 0.292916 0.009515 30.784 < 2e-16 ***
## marital_statusmarried 0.600320 0.181038 3.316 0.000913 ***
## marital_statusnotmarried -0.540404 0.088636 -6.097 1.08e-09 ***
## marital_statuswidowed 0.065110 0.161934 0.402 0.687626
## occupationclerical 1.085092 0.155798 6.965 3.29e-12 ***
## occupationhighskill 1.638349 0.139905 11.710 < 2e-16 ***
## occupationlowskill 0.246583 0.162845 1.514 0.129971
## occupationmidskill 1.060318 0.141711 7.482 7.31e-14 ***
## occupationother 12.439475 136.264650 0.091 0.927263
## household_rolewife 1.296667 0.109590 11.832 < 2e-16 ***
## household_roletenant -0.961196 0.177802 -5.406 6.44e-08 ***
## household_rolesingle_occ -1.171448 0.198074 -5.914 3.34e-09 ***
## household_rolerelative -1.516062 0.255368 -5.937 2.91e-09 ***
## household_roledepchild -1.971781 0.218628 -9.019 < 2e-16 ***
## gendermale 0.809687 0.082792 9.780 < 2e-16 ***
## hours_per_week 0.030398 0.001765 17.226 < 2e-16 ***
## native_regionEAsia 0.438599 0.189835 2.310 0.020865 *
## native_regionEurope 0.454797 0.181959 2.499 0.012439 *
## native_regionNorthAmerica 0.435257 0.119914 3.630 0.000284 ***
## native_regionSAsia -0.022621 0.292872 -0.077 0.938435
## native_regionSouthAmerica -0.686574 0.340314 -2.017 0.043646 *
## native_regionUSTerritory -0.205661 0.414099 -0.497 0.619437
## capital_gain_log 0.211036 0.007245 29.129 < 2e-16 ***
## capital_loss_log 0.155592 0.010562 14.731 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28679 on 26049 degrees of freedom
## Residual deviance: 17152 on 26017 degrees of freedom
## AIC: 17218
##
## Number of Fisher Scoring iterations: 12# Model accuracy
accuracy <- mean(predicted.classes==testing_set$income)
# Calculate Sensitivity (True Positive Rate)
sensitivity <- tb[2, 2] / (tb[2, 2] + tb[1, 2])
# Calculate Specificity (True Negative Rate)
specificity <- tb[1, 1] / (tb[1, 1] + tb[2, 1])
# Calculate Prevalence
prevalence <- (tb[1, 2] + tb[2, 2]) / sum(tb)
# Calculate PPV (Positive Predictive Value)
ppv <- tb[2, 2] / (tb[2, 2] + tb[2, 1])
# Calculate NPV (Negative Predictive Value)
npv <- tb[1, 1] / (tb[1, 1] + tb[1, 2])
# Calculate AUROC
roc_obj <- roc(testing_set$income, probabilities)## Setting levels: control = <=50K, case = >50K
## Setting direction: controls < casesauroc <- auc(roc_obj)
# Print the metrics
cat("accuracy:", accuracy, "\n")## accuracy: 0.7972973cat("Sensitivity:", sensitivity, "\n")## Sensitivity: 0.8334373cat("Specificity:", specificity, "\n")## Specificity: 0.785496cat("Prevalence:", prevalence, "\n")## Prevalence: 0.2461609cat("PPV:", ppv, "\n")## PPV: 0.5592298cat("NPV:", npv, "\n")## NPV: 0.9352413cat("AUROC:", auroc, "\n")## AUROC: 0.8943199Basic Stepwise: backward, forward, both
Note: There was no difference in the models suggesting the model complexity is already as simple as possibly * The one-hot-encoding of categorical variables may also be impacting the selection process.
cat("\n\n--------Backward Elimination---------\n\n")##
##
## --------Backward Elimination---------# backward selection
step(full.model, trace = F, scope = list(lower=formula(full.model), upper=formula(full.model)),
direction = 'backward')##
## Call: glm(formula = income ~ age + workclass + education_level + years_education +
## marital_status + occupation + household_role + race + gender +
## hours_per_week + native_region + capital_gain_log + capital_loss_log,
## family = binomial, data = training_set)
##
## Coefficients:
## (Intercept) age
## -21.89895 0.02534
## workclassFedGov workclassLocGov
## 13.55632 12.85409
## workclassNeverWorked workclassPrivate
## -10.62424 13.03853
## workclassSelfEmpInc workclassSelfEmpNotInc
## 13.29227 12.56798
## workclassStateGov education_levelhighsch
## 12.80217 -0.12944
## education_levelmaster education_levelno_edu
## 0.16680 -10.21910
## education_levelphd education_levelprimary
## 0.20818 0.06427
## education_levelsecondary education_levelundergrad
## -0.45394 0.06948
## years_education marital_statusmarried
## 0.25242 0.60322
## marital_statusnotmarried marital_statuswidowed
## -0.54349 0.06997
## occupationclerical occupationhighskill
## 1.08992 1.63681
## occupationlowskill occupationmidskill
## 0.25307 1.07020
## occupationother household_rolewife
## 13.43787 1.30301
## household_roletenant household_rolesingle_occ
## -0.95624 -1.16494
## household_rolerelative household_roledepchild
## -1.50598 -1.96856
## racewhite raceblack
## 0.28215 0.24280
## racenative raceAsian_Pac_Islander
## -0.14146 0.34776
## gendermale hours_per_week
## 0.80952 0.03039
## native_regionEAsia native_regionEurope
## 0.37625 0.44993
## native_regionNorthAmerica native_regionSAsia
## 0.43823 -0.05089
## native_regionSouthAmerica native_regionUSTerritory
## -0.70621 -0.15851
## capital_gain_log capital_loss_log
## 0.21143 0.15543
##
## Degrees of Freedom: 26049 Total (i.e. Null); 26006 Residual
## Null Deviance: 28680
## Residual Deviance: 17140 AIC: 17230cat("\n\n--------Forward Elimination---------\n\n")##
##
## --------Forward Elimination---------# forward selection
step(full.model, trace = F, scope = list(lower=formula(full.model), upper=formula(full.model)),
direction = 'forward')##
## Call: glm(formula = income ~ age + workclass + education_level + years_education +
## marital_status + occupation + household_role + race + gender +
## hours_per_week + native_region + capital_gain_log + capital_loss_log,
## family = binomial, data = training_set)
##
## Coefficients:
## (Intercept) age
## -21.89895 0.02534
## workclassFedGov workclassLocGov
## 13.55632 12.85409
## workclassNeverWorked workclassPrivate
## -10.62424 13.03853
## workclassSelfEmpInc workclassSelfEmpNotInc
## 13.29227 12.56798
## workclassStateGov education_levelhighsch
## 12.80217 -0.12944
## education_levelmaster education_levelno_edu
## 0.16680 -10.21910
## education_levelphd education_levelprimary
## 0.20818 0.06427
## education_levelsecondary education_levelundergrad
## -0.45394 0.06948
## years_education marital_statusmarried
## 0.25242 0.60322
## marital_statusnotmarried marital_statuswidowed
## -0.54349 0.06997
## occupationclerical occupationhighskill
## 1.08992 1.63681
## occupationlowskill occupationmidskill
## 0.25307 1.07020
## occupationother household_rolewife
## 13.43787 1.30301
## household_roletenant household_rolesingle_occ
## -0.95624 -1.16494
## household_rolerelative household_roledepchild
## -1.50598 -1.96856
## racewhite raceblack
## 0.28215 0.24280
## racenative raceAsian_Pac_Islander
## -0.14146 0.34776
## gendermale hours_per_week
## 0.80952 0.03039
## native_regionEAsia native_regionEurope
## 0.37625 0.44993
## native_regionNorthAmerica native_regionSAsia
## 0.43823 -0.05089
## native_regionSouthAmerica native_regionUSTerritory
## -0.70621 -0.15851
## capital_gain_log capital_loss_log
## 0.21143 0.15543
##
## Degrees of Freedom: 26049 Total (i.e. Null); 26006 Residual
## Null Deviance: 28680
## Residual Deviance: 17140 AIC: 17230cat("\n\n--------Both Elimination---------\n\n")##
##
## --------Both Elimination---------# forward selection
step(full.model, trace = F, scope = list(lower=formula(full.model), upper=formula(full.model)),
direction = 'both')##
## Call: glm(formula = income ~ age + workclass + education_level + years_education +
## marital_status + occupation + household_role + race + gender +
## hours_per_week + native_region + capital_gain_log + capital_loss_log,
## family = binomial, data = training_set)
##
## Coefficients:
## (Intercept) age
## -21.89895 0.02534
## workclassFedGov workclassLocGov
## 13.55632 12.85409
## workclassNeverWorked workclassPrivate
## -10.62424 13.03853
## workclassSelfEmpInc workclassSelfEmpNotInc
## 13.29227 12.56798
## workclassStateGov education_levelhighsch
## 12.80217 -0.12944
## education_levelmaster education_levelno_edu
## 0.16680 -10.21910
## education_levelphd education_levelprimary
## 0.20818 0.06427
## education_levelsecondary education_levelundergrad
## -0.45394 0.06948
## years_education marital_statusmarried
## 0.25242 0.60322
## marital_statusnotmarried marital_statuswidowed
## -0.54349 0.06997
## occupationclerical occupationhighskill
## 1.08992 1.63681
## occupationlowskill occupationmidskill
## 0.25307 1.07020
## occupationother household_rolewife
## 13.43787 1.30301
## household_roletenant household_rolesingle_occ
## -0.95624 -1.16494
## household_rolerelative household_roledepchild
## -1.50598 -1.96856
## racewhite raceblack
## 0.28215 0.24280
## racenative raceAsian_Pac_Islander
## -0.14146 0.34776
## gendermale hours_per_week
## 0.80952 0.03039
## native_regionEAsia native_regionEurope
## 0.37625 0.44993
## native_regionNorthAmerica native_regionSAsia
## 0.43823 -0.05089
## native_regionSouthAmerica native_regionUSTerritory
## -0.70621 -0.15851
## capital_gain_log capital_loss_log
## 0.21143 0.15543
##
## Degrees of Freedom: 26049 Total (i.e. Null); 26006 Residual
## Null Deviance: 28680
## Residual Deviance: 17140 AIC: 17230Stepwise for influencial outliers
cat("\n\n--------Backward Elimination---------\n\n")##
##
## --------Backward Elimination---------# backward selection
step(model4, trace = F, scope = list(lower=formula(model4), upper=formula(model4)),
direction = 'backward')##
## Call: glm(formula = income ~ age + workclass + education_level + years_education +
## marital_status + occupation + household_role + race + gender +
## hours_per_week + native_region + capital_gain_log + capital_loss_log,
## family = binomial, data = training_set_out)
##
## Coefficients:
## (Intercept) age
## -21.57250 0.02565
## workclassFedGov workclassLocGov
## 12.88731 12.14860
## workclassNeverWorked workclassPrivate
## -10.62443 12.32773
## workclassSelfEmpInc workclassSelfEmpNotInc
## 12.53429 11.86370
## workclassStateGov education_levelhighsch
## 12.04891 -0.11809
## education_levelmaster education_levelno_edu
## 0.22180 -10.42375
## education_levelphd education_levelprimary
## 0.36933 0.03612
## education_levelsecondary education_levelundergrad
## -0.38775 0.07748
## years_education marital_statusmarried
## 0.25381 0.78187
## marital_statusnotmarried marital_statuswidowed
## -0.45543 0.20167
## occupationclerical occupationhighskill
## 0.91164 1.50236
## occupationlowskill occupationmidskill
## 0.13007 0.91050
## occupationother household_rolewife
## 12.57631 1.29026
## household_roletenant household_rolesingle_occ
## -0.83068 -1.14693
## household_rolerelative household_roledepchild
## -1.20774 -1.97223
## racewhite raceblack
## 0.59108 0.54800
## racenative raceAsian_Pac_Islander
## -0.03787 0.84575
## gendermale hours_per_week
## 0.80497 0.03023
## native_regionEAsia native_regionEurope
## 0.17525 0.54364
## native_regionNorthAmerica native_regionSAsia
## 0.46042 -0.23978
## native_regionSouthAmerica native_regionUSTerritory
## -0.70149 -0.23933
## capital_gain_log capital_loss_log
## 0.20890 0.14435
##
## Degrees of Freedom: 26037 Total (i.e. Null); 25994 Residual
## Null Deviance: 28590
## Residual Deviance: 17150 AIC: 17230cat("\n\n--------Forward Elimination---------\n\n")##
##
## --------Forward Elimination---------# forward selection
step(model4, trace = F, scope = list(lower=formula(model4), upper=formula(model4)),
direction = 'forward')##
## Call: glm(formula = income ~ age + workclass + education_level + years_education +
## marital_status + occupation + household_role + race + gender +
## hours_per_week + native_region + capital_gain_log + capital_loss_log,
## family = binomial, data = training_set_out)
##
## Coefficients:
## (Intercept) age
## -21.57250 0.02565
## workclassFedGov workclassLocGov
## 12.88731 12.14860
## workclassNeverWorked workclassPrivate
## -10.62443 12.32773
## workclassSelfEmpInc workclassSelfEmpNotInc
## 12.53429 11.86370
## workclassStateGov education_levelhighsch
## 12.04891 -0.11809
## education_levelmaster education_levelno_edu
## 0.22180 -10.42375
## education_levelphd education_levelprimary
## 0.36933 0.03612
## education_levelsecondary education_levelundergrad
## -0.38775 0.07748
## years_education marital_statusmarried
## 0.25381 0.78187
## marital_statusnotmarried marital_statuswidowed
## -0.45543 0.20167
## occupationclerical occupationhighskill
## 0.91164 1.50236
## occupationlowskill occupationmidskill
## 0.13007 0.91050
## occupationother household_rolewife
## 12.57631 1.29026
## household_roletenant household_rolesingle_occ
## -0.83068 -1.14693
## household_rolerelative household_roledepchild
## -1.20774 -1.97223
## racewhite raceblack
## 0.59108 0.54800
## racenative raceAsian_Pac_Islander
## -0.03787 0.84575
## gendermale hours_per_week
## 0.80497 0.03023
## native_regionEAsia native_regionEurope
## 0.17525 0.54364
## native_regionNorthAmerica native_regionSAsia
## 0.46042 -0.23978
## native_regionSouthAmerica native_regionUSTerritory
## -0.70149 -0.23933
## capital_gain_log capital_loss_log
## 0.20890 0.14435
##
## Degrees of Freedom: 26037 Total (i.e. Null); 25994 Residual
## Null Deviance: 28590
## Residual Deviance: 17150 AIC: 17230cat("\n\n--------Both Elimination---------\n\n")##
##
## --------Both Elimination---------# forward selection
step(model4, trace = F, scope = list(lower=formula(model4), upper=formula(model4)),
direction = 'both')##
## Call: glm(formula = income ~ age + workclass + education_level + years_education +
## marital_status + occupation + household_role + race + gender +
## hours_per_week + native_region + capital_gain_log + capital_loss_log,
## family = binomial, data = training_set_out)
##
## Coefficients:
## (Intercept) age
## -21.57250 0.02565
## workclassFedGov workclassLocGov
## 12.88731 12.14860
## workclassNeverWorked workclassPrivate
## -10.62443 12.32773
## workclassSelfEmpInc workclassSelfEmpNotInc
## 12.53429 11.86370
## workclassStateGov education_levelhighsch
## 12.04891 -0.11809
## education_levelmaster education_levelno_edu
## 0.22180 -10.42375
## education_levelphd education_levelprimary
## 0.36933 0.03612
## education_levelsecondary education_levelundergrad
## -0.38775 0.07748
## years_education marital_statusmarried
## 0.25381 0.78187
## marital_statusnotmarried marital_statuswidowed
## -0.45543 0.20167
## occupationclerical occupationhighskill
## 0.91164 1.50236
## occupationlowskill occupationmidskill
## 0.13007 0.91050
## occupationother household_rolewife
## 12.57631 1.29026
## household_roletenant household_rolesingle_occ
## -0.83068 -1.14693
## household_rolerelative household_roledepchild
## -1.20774 -1.97223
## racewhite raceblack
## 0.59108 0.54800
## racenative raceAsian_Pac_Islander
## -0.03787 0.84575
## gendermale hours_per_week
## 0.80497 0.03023
## native_regionEAsia native_regionEurope
## 0.17525 0.54364
## native_regionNorthAmerica native_regionSAsia
## 0.46042 -0.23978
## native_regionSouthAmerica native_regionUSTerritory
## -0.70149 -0.23933
## capital_gain_log capital_loss_log
## 0.20890 0.14435
##
## Degrees of Freedom: 26037 Total (i.e. Null); 25994 Residual
## Null Deviance: 28590
## Residual Deviance: 17150 AIC: 17230Reduced model that eliminates non significant variables and colinearity between those variables.
new_formula2 <- as.formula(income ~ +age + years_education + capital_gain_log + capital_loss_log +
marital_status+ occupation + household_role + gender + race + hours_per_week)
new_formula2## income ~ +age + years_education + capital_gain_log + capital_loss_log +
## marital_status + occupation + household_role + gender + race +
## hours_per_week# Fit the reduced model
reduced_model2 <- glm(new_formula2, data = training_set, family = binomial)
summary(reduced_model2)##
## Call:
## glm(formula = new_formula2, family = binomial, data = training_set)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -9.179476 0.403223 -22.765 < 2e-16 ***
## age 0.024479 0.001732 14.135 < 2e-16 ***
## years_education 0.287354 0.009373 30.658 < 2e-16 ***
## capital_gain_log 0.210640 0.007205 29.235 < 2e-16 ***
## capital_loss_log 0.155009 0.010483 14.787 < 2e-16 ***
## marital_statusmarried 0.550021 0.180209 3.052 0.002272 **
## marital_statusnotmarried -0.544400 0.088346 -6.162 7.18e-10 ***
## marital_statuswidowed 0.073068 0.161611 0.452 0.651179
## occupationclerical 1.378467 0.152101 9.063 < 2e-16 ***
## occupationhighskill 1.843598 0.137544 13.404 < 2e-16 ***
## occupationlowskill 0.452575 0.160270 2.824 0.004745 **
## occupationmidskill 1.260004 0.139171 9.054 < 2e-16 ***
## occupationother 0.660050 0.172593 3.824 0.000131 ***
## household_rolewife 1.256316 0.108977 11.528 < 2e-16 ***
## household_roletenant -0.999513 0.176982 -5.648 1.63e-08 ***
## household_rolesingle_occ -1.222040 0.197550 -6.186 6.17e-10 ***
## household_rolerelative -1.585747 0.254463 -6.232 4.61e-10 ***
## household_roledepchild -2.014765 0.218107 -9.238 < 2e-16 ***
## gendermale 0.801219 0.082452 9.717 < 2e-16 ***
## racewhite 0.483374 0.278467 1.736 0.082592 .
## raceblack 0.445563 0.287955 1.547 0.121782
## racenative 0.065361 0.365183 0.179 0.857953
## raceAsian_Pac_Islander 0.402804 0.297828 1.352 0.176224
## hours_per_week 0.030445 0.001737 17.529 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28679 on 26049 degrees of freedom
## Residual deviance: 17284 on 26026 degrees of freedom
## AIC: 17332
##
## Number of Fisher Scoring iterations: 7# Check for multicollinearity
vif(reduced_model2) ## GVIF Df GVIF^(1/(2*Df))
## age 1.200225 1 1.095548
## years_education 1.293949 1 1.137519
## capital_gain_log 1.028938 1 1.014366
## capital_loss_log 1.011269 1 1.005619
## marital_status 18.658428 3 1.628593
## occupation 1.519827 5 1.042748
## household_role 41.900493 5 1.452854
## gender 2.785419 1 1.668957
## race 1.044452 4 1.005451
## hours_per_week 1.117688 1 1.057208# Perform Hosmer-Lemeshow goodness of fit test
# Note: A p-value less than 0.05 typically suggests that the model does not fit the data well.
hoslem.test(training_set$income, fitted(reduced_model2))## Warning in Ops.factor(1, y): '-' not meaningful for factors##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: training_set$income, fitted(reduced_model2)
## X-squared = 26050, df = 8, p-value < 2.2e-16# Make predictions
probabilities <- reduced_model2 %>% predict(testing_set, type = "response")
predicted.classes <- ifelse(probabilities > 0.27, ">50K", "<=50K")
# Model accuracy
accuracy <- mean(predicted.classes==testing_set$income)
# confusion matrix
tb <- table(predicted.classes, testing_set$income)
tb##
## predicted.classes <=50K >50K
## <=50K 3975 314
## >50K 934 1289# Calculate Sensitivity (True Positive Rate)
sensitivity <- tb[2, 2] / (tb[2, 2] + tb[1, 2])
# Calculate Specificity (True Negative Rate)
specificity <- tb[1, 1] / (tb[1, 1] + tb[2, 1])
# Calculate Prevalence
prevalence <- (tb[1, 2] + tb[2, 2]) / sum(tb)
# Calculate PPV (Positive Predictive Value)
ppv <- tb[2, 2] / (tb[2, 2] + tb[2, 1])
# Calculate NPV (Negative Predictive Value)
npv <- tb[1, 1] / (tb[1, 1] + tb[1, 2])
# Calculate AUROC
roc_obj <- roc(testing_set$income, probabilities)## Setting levels: control = <=50K, case = >50K## Setting direction: controls < casesauroc <- auc(roc_obj)
# Print the metrics
cat("accuracy:", accuracy, "\n")## accuracy: 0.8083538cat("Sensitivity:", sensitivity, "\n")## Sensitivity: 0.8041173cat("Specificity:", specificity, "\n")## Specificity: 0.8097372cat("Prevalence:", prevalence, "\n")## Prevalence: 0.2461609cat("PPV:", ppv, "\n")## PPV: 0.5798471cat("NPV:", npv, "\n")## NPV: 0.9267895cat("AUROC:", auroc, "\n")## AUROC: 0.8934422############### Outliers Removed #############################
# Fit the reduced model
reduced_model3 <- glm(new_formula2, data = training_set_out, family = binomial)
summary(reduced_model2)##
## Call:
## glm(formula = new_formula2, family = binomial, data = training_set)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -9.179476 0.403223 -22.765 < 2e-16 ***
## age 0.024479 0.001732 14.135 < 2e-16 ***
## years_education 0.287354 0.009373 30.658 < 2e-16 ***
## capital_gain_log 0.210640 0.007205 29.235 < 2e-16 ***
## capital_loss_log 0.155009 0.010483 14.787 < 2e-16 ***
## marital_statusmarried 0.550021 0.180209 3.052 0.002272 **
## marital_statusnotmarried -0.544400 0.088346 -6.162 7.18e-10 ***
## marital_statuswidowed 0.073068 0.161611 0.452 0.651179
## occupationclerical 1.378467 0.152101 9.063 < 2e-16 ***
## occupationhighskill 1.843598 0.137544 13.404 < 2e-16 ***
## occupationlowskill 0.452575 0.160270 2.824 0.004745 **
## occupationmidskill 1.260004 0.139171 9.054 < 2e-16 ***
## occupationother 0.660050 0.172593 3.824 0.000131 ***
## household_rolewife 1.256316 0.108977 11.528 < 2e-16 ***
## household_roletenant -0.999513 0.176982 -5.648 1.63e-08 ***
## household_rolesingle_occ -1.222040 0.197550 -6.186 6.17e-10 ***
## household_rolerelative -1.585747 0.254463 -6.232 4.61e-10 ***
## household_roledepchild -2.014765 0.218107 -9.238 < 2e-16 ***
## gendermale 0.801219 0.082452 9.717 < 2e-16 ***
## racewhite 0.483374 0.278467 1.736 0.082592 .
## raceblack 0.445563 0.287955 1.547 0.121782
## racenative 0.065361 0.365183 0.179 0.857953
## raceAsian_Pac_Islander 0.402804 0.297828 1.352 0.176224
## hours_per_week 0.030445 0.001737 17.529 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28679 on 26049 degrees of freedom
## Residual deviance: 17284 on 26026 degrees of freedom
## AIC: 17332
##
## Number of Fisher Scoring iterations: 7# Check for multicollinearity
vif(reduced_model3) ## GVIF Df GVIF^(1/(2*Df))
## age 1.209039 1 1.099563
## years_education 1.303372 1 1.141653
## capital_gain_log 1.029705 1 1.014744
## capital_loss_log 1.011626 1 1.005796
## marital_status 18.694621 3 1.629119
## occupation 1.519776 5 1.042745
## household_role 41.172083 5 1.450308
## gender 2.782263 1 1.668012
## race 1.041505 4 1.005096
## hours_per_week 1.113945 1 1.055436# Perform Hosmer-Lemeshow goodness of fit test
# Note: A p-value less than 0.05 typically suggests that the model does not fit the data well.
hoslem.test(training_set_out$income, fitted(reduced_model3))## Warning in Ops.factor(1, y): '-' not meaningful for factors##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: training_set_out$income, fitted(reduced_model3)
## X-squared = 26038, df = 8, p-value < 2.2e-16# Make predictions
probabilities2 <- reduced_model3 %>% predict(testing_set_out, type = "response")
predicted.classes2 <- ifelse(probabilities2 > 0.27, ">50K", "<=50K")
# Model accuracy
accuracy <- mean(predicted.classes2==testing_set_out$income)
# confusion matrix
tb2 <- table(predicted.classes2, testing_set_out$income)
tb2##
## predicted.classes2 <=50K >50K
## <=50K 3986 284
## >50K 901 1338# Calculate Sensitivity (True Positive Rate)
sensitivity2 <- tb2[2, 2] / (tb2[2, 2] + tb2[1, 2])
# Calculate Specificity (True Negative Rate)
specificity2 <- tb2[1, 1] / (tb2[1, 1] + tb2[2, 1])
# Calculate Prevalence
prevalence2 <- (tb2[1, 2] + tb2[2, 2]) / sum(tb2)
# Calculate PPV (Positive Predictive Value)
ppv2 <- tb2[2, 2] / (tb2[2, 2] + tb2[2, 1])
# Calculate NPV (Negative Predictive Value)
npv2 <- tb2[1, 1] / (tb2[1, 1] + tb2[1, 2])
# Calculate AUROC
roc_obj2 <- roc(testing_set_out$income, probabilities2)## Setting levels: control = <=50K, case = >50K
## Setting direction: controls < casesauroc2 <- auc(roc_obj)
# Print the metrics
cat("accuracy:", accuracy2, "\n")## accuracy: 0.8210171cat("Sensitivity:", sensitivity2, "\n")## Sensitivity: 0.8249075cat("Specificity:", specificity2, "\n")## Specificity: 0.8156333cat("Prevalence:", prevalence2, "\n")## Prevalence: 0.2491934cat("PPV:", ppv2, "\n")## PPV: 0.5975882cat("NPV:", npv2, "\n")## NPV: 0.9334895cat("AUROC:", auroc2, "\n")## AUROC: 0.8934422# full with outliers
pred_full <- prediction(probabilities, testing_set$income)
pred_full_per <- performance(pred_full, measure = "tpr", x.measure = "fpr")
mod1_pred <- data.frame(FP = pred_full_per@x.values[[1]], TP = pred_full_per@y.values[[1]])
# full without outliers
pred_full_Out <- prediction(probabilities2, testing_set_out$income)
pred_full_per2 <- performance(pred_full_Out, measure = "tpr", x.measure = "fpr")
mod2_pred <- data.frame(FP = pred_full_per2@x.values[[1]], TP = pred_full_per2@y.values[[1]])
# plot ROC curve for logistic regression
g <- ggplot() +
geom_line(data = mod1_pred, aes(x = FP, y = TP, color = 'Reduced With Outliers')) +
geom_line(data = mod2_pred, aes(x = FP, y = TP, color = 'Reduced Without Outliers')) +
geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1)) +
ggtitle('ROC Curve') +
labs(x = 'False Positive Rate', y = 'True Positive Rate')
g + scale_colour_manual(name = 'Classifier', values = c('Reduced With Outliers'='red',
'Reduced Without Outliers'='blue'))
Odds ratio for the reduced model removing influencial outliers.
An OR of x suggests that with each additional year of (the variable), the odds of the outcome occurring increase by approximately ((OR -1)*100) all other variables remaining constant. or if it has a reference variable…compared to the reference variable.
summary(reduced_model3)##
## Call:
## glm(formula = new_formula2, family = binomial, data = training_set_out)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -9.598491 0.418492 -22.936 < 2e-16 ***
## age 0.024997 0.001730 14.446 < 2e-16 ***
## years_education 0.291136 0.009383 31.028 < 2e-16 ***
## capital_gain_log 0.208743 0.007156 29.171 < 2e-16 ***
## capital_loss_log 0.143913 0.010389 13.852 < 2e-16 ***
## marital_statusmarried 0.738096 0.179356 4.115 3.87e-05 ***
## marital_statusnotmarried -0.448284 0.088284 -5.078 3.82e-07 ***
## marital_statuswidowed 0.194245 0.159523 1.218 0.22335
## occupationclerical 1.197102 0.147693 8.105 5.26e-16 ***
## occupationhighskill 1.699542 0.133246 12.755 < 2e-16 ***
## occupationlowskill 0.327374 0.155885 2.100 0.03572 *
## occupationmidskill 1.098318 0.134801 8.148 3.71e-16 ***
## occupationother 0.510718 0.168592 3.029 0.00245 **
## household_rolewife 1.240202 0.108733 11.406 < 2e-16 ***
## household_roletenant -0.857469 0.175185 -4.895 9.85e-07 ***
## household_rolesingle_occ -1.185394 0.197134 -6.013 1.82e-09 ***
## household_rolerelative -1.280702 0.231889 -5.523 3.33e-08 ***
## household_roledepchild -2.013056 0.221612 -9.084 < 2e-16 ***
## gendermale 0.790859 0.082095 9.634 < 2e-16 ***
## racewhite 0.827866 0.299933 2.760 0.00578 **
## raceblack 0.777479 0.308565 2.520 0.01175 *
## racenative 0.220347 0.384144 0.574 0.56624
## raceAsian_Pac_Islander 0.779149 0.317370 2.455 0.01409 *
## hours_per_week 0.030230 0.001743 17.342 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28594 on 26037 degrees of freedom
## Residual deviance: 17295 on 26014 degrees of freedom
## AIC: 17343
##
## Number of Fisher Scoring iterations: 7# Calculate Odds Ratios and 90% Confidence Intervals
odds_ratios <- exp(coef(reduced_model3))
conf_int <- exp(confint(reduced_model3, level = 0.95))## Waiting for profiling to be done...# Identify significant predictors at the 0.05 level
significant_predictors <- summary(reduced_model3)$coefficients[, 4] < 0.05
# Extract Odds Ratios and Confidence Intervals for significant predictors
# Note: Use names() to match significant predictors by name
significant_or_ci <- data.frame(
OddsRatio = odds_ratios[significant_predictors],
LowerCI = conf_int[names(odds_ratios)[significant_predictors], 1],
UpperCI = conf_int[names(odds_ratios)[significant_predictors], 2]
)
significant_or_ci## OddsRatio LowerCI UpperCI
## (Intercept) 6.783099e-05 2.936562e-05 0.0001518311
## age 1.025312e+00 1.021844e+00 1.0287992244
## years_education 1.337946e+00 1.313673e+00 1.3628928306
## capital_gain_log 1.232128e+00 1.215050e+00 1.2496192802
## capital_loss_log 1.154783e+00 1.131548e+00 1.1785880768
## marital_statusmarried 2.091949e+00 1.461510e+00 2.9543600056
## marital_statusnotmarried 6.387232e-01 5.372228e-01 0.7594162224
## occupationclerical 3.310510e+00 2.487612e+00 4.4400246538
## occupationhighskill 5.471442e+00 4.233618e+00 7.1406257944
## occupationlowskill 1.387320e+00 1.024740e+00 1.8888283730
## occupationmidskill 2.999117e+00 2.313470e+00 3.9258245123
## occupationother 1.666487e+00 1.199673e+00 2.3241718290
## household_rolewife 3.456311e+00 2.794177e+00 4.2794144445
## household_roletenant 4.242344e-01 2.987118e-01 0.5940437539
## household_rolesingle_occ 3.056257e-01 2.062195e-01 0.4468625819
## household_rolerelative 2.778421e-01 1.734786e-01 0.4314173322
## household_roledepchild 1.335798e-01 8.560423e-02 0.2042279905
## gendermale 2.205289e+00 1.878715e+00 2.5920962860
## racewhite 2.288431e+00 1.304018e+00 4.2419327604
## raceblack 2.175980e+00 1.217390e+00 4.0962024388
## raceAsian_Pac_Islander 2.179618e+00 1.196645e+00 4.1677149687
## hours_per_week 1.030692e+00 1.027184e+00 1.0342276616Most of the residuals fall within +/- 3
# create a data frame to store information regarding deviance residuals
index <- 1:dim(training_set)[1]
dev_resid <- residuals(model3)
income <- training_set$income
dff <- data.frame(index, dev_resid, income)
ggplot(dff, aes(x = index, y = dev_resid, color = income)) +
geom_point() +
geom_hline(yintercept = 3, linetype = 'dashed', color = 'blue') +
geom_hline(yintercept = -3, linetype = 'dashed', color = 'blue')
Objective 1: Simple GLM and Interpretation
# Set a seed for reproducibility
set.seed(1234)
# Calculate the number of rows to select
sample_size2 <- round(0.8 * nrow(adult3))
# Create a random sample of indices
index2 <- sample(1:nrow(adult3), size = sample_size2, replace = FALSE)
# Create the training set using the selected indices
training_set2 <- adult3[index, ]
# Create the testing set with the remaining indices
testing_set2 <- adult3[-index, ]
nrow(training_set2)## [1] 26050nrow(testing_set2)## [1] 6512Full Model (With Outliers)
set.seed(1234)
# Logging Capital
## Train
capital_data_training<-training_set2[,c("capital_gain","capital_loss")]
capital_data_training <- capital_data_training %>%
mutate(
capital_gain = as.numeric(capital_gain) + 0.000001,
capital_loss = as.numeric(capital_loss) + 0.000001
) %>%
mutate(log_capital_gain = log(capital_gain),
log_capital_loss = log(capital_loss)
)
training_set2<-cbind(training_set2, capital_data_training)
# Assigning classes in transformed dataset
training_set2$age<-as.numeric(training_set2$age)
training_set2$workclass<-factor(training_set2$workclass)
training_set2$education_level<-factor(training_set2$education_level)
training_set2$years_education<-as.numeric(training_set2$years_education)
training_set2$marital_status<-factor(training_set2$marital_status)
training_set2$occupation<-factor(training_set2$occupation)
training_set2$household_role<-factor(training_set2$household_role)
#training_set$continent<-factor(training_set$continent)
training_set$capital_loss_log<-as.numeric(training_set$capital_loss_log)
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## [26029] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0## Test
capital_data_testing <- testing_set2[, c("capital_gain", "capital_loss")]
capital_data_testing <- capital_data_testing %>%
mutate(
capital_gain = as.numeric(capital_gain) + 0.000001,
capital_loss = as.numeric(capital_loss) + 0.000001
) %>%
mutate(log_capital_gain = log(capital_gain),
log_capital_loss = log(capital_loss)
)
testing_set2 <- cbind(testing_set2, capital_data_testing)
# Assigning classes in transformed dataset
testing_set2$age <- as.numeric(testing_set2$age)
testing_set2$workclass <- factor(testing_set2$workclass)
testing_set2$education_level <- factor(testing_set2$education_level)
testing_set2$years_education <- as.numeric(testing_set2$years_education)
testing_set2$marital_status <- factor(testing_set2$marital_status)
testing_set2$occupation <- factor(testing_set2$occupation)
testing_set2$household_role <- factor(testing_set2$household_role)
#testing_set$continent <- factor(testing_set$continent)
testing_set$capital_loss_log <- as.numeric(testing_set$capital_loss_log)
testing_set$capital_gain_log <- as.numeric(testing_set$capital_gain_log)
glm_final<-glm(income.num~age+workclass+education_level+years_education+marital_status+occupation+household_role+race+native_region+capital_loss_log+capital_gain_log, data=training_set2, family="binomial")
# summary(glm_final)
# cbind("Coefficients" = coef(glm_final), #confint.default(glm_final, level=0.95))ROC: Full Model With Outliers
set.seed(1234)
glm_final<-glm(income.num~age+workclass+education_level+years_education+marital_status+occupation+household_role+race+native_region+log_capital_loss+log_capital_gain, data=training_set2, family="binomial")
predicted_probs_final <- predict(glm_final, newdata = testing_set2, type = "response")
testing_set2$income.num <- ifelse(testing_set2$income == ">50K", 1, 0)
roc_curve_final <- roc(testing_set2$income, predicted_probs_final)## Setting levels: control = <=50K, case = >50K## Setting direction: controls < casesoptimal_threshold_final<- coords(roc_curve_final, "best", best.method = "closest.topleft")
plot(roc_curve_final, main = "ROC Curve With Outliers", col = "blue")
abline(a = 0, b = 1, lty = 2, col = "red")
legend("bottomright", legend = paste("AUC =", round(auc(roc_curve_final), 5)), col = "blue", lwd = 2)
AUROC of glm model with outliers is 88.781%
Metrics: Full Model with Outliers
set.seed(1234)
glm_final<-glm(income.num~age+workclass+education_level+years_education+marital_status+occupation+household_role+race+native_region+log_capital_loss+log_capital_gain, data=training_set2, family="binomial")
testing_set2<-cbind(testing_set2, capital_data_testing)
predicted_probs_final <- predict(glm_final, newdata = testing_set2, type = "response")
testing_set2$income.num <- ifelse(testing_set2$income == "<=50K", 1, 0)
predicted_classes_final <- ifelse(predicted_probs_final < optimal_threshold_final$threshold,1, 0)
result_df <- data.frame(Index = as.numeric(names(predicted_classes_final)),
Predicted_Class = as.factor(predicted_classes_final))
confusion_matrix_final <- confusionMatrix(result_df$Predicted_Class, factor(testing_set2$income.num))
confusion_matrix_final## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1343 1000
## 1 257 3912
##
## Accuracy : 0.807
## 95% CI : (0.7972, 0.8165)
## No Information Rate : 0.7543
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5497
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.8394
## Specificity : 0.7964
## Pos Pred Value : 0.5732
## Neg Pred Value : 0.9384
## Prevalence : 0.2457
## Detection Rate : 0.2062
## Detection Prevalence : 0.3598
## Balanced Accuracy : 0.8179
##
## 'Positive' Class : 0
## Reduced Model (With Outliers)
set.seed(1234)
glm_final<-glm(income.num~age+years_education+marital_status+occupation+household_role+gender+race+log_capital_loss+log_capital_gain, data=training_set2, family="binomial")
#summary(glm_final)ROC: Reduced Model With Outliers
set.seed(1234)
# Reduced Model
glm_final<-glm(income.num~age+years_education+marital_status+occupation+household_role+gender+race+capital_loss_log+capital_gain_log, data=training_set2, family="binomial")
predicted_probs_final <- predict(glm_final, newdata = testing_set2, type = "response")
testing_set2$income.num <- ifelse(testing_set2$income == ">50K", 1, 0)
roc_curve_final <- roc(testing_set2$income, predicted_probs_final)## Setting levels: control = <=50K, case = >50K## Setting direction: controls < casesoptimal_threshold_final<- coords(roc_curve_final, "best", best.method = "closest.topleft")
plot(roc_curve_final, main = "ROC Curve of Reduced Model With Outliers", col = "blue")
abline(a = 0, b = 1, lty = 2, col = "red")
legend("bottomright", legend = paste("AUC =", round(auc(roc_curve_final), 5)), col = "blue", lwd = 2)
Metrics: Reduced Model With Outliers
# Reduced Model
glm_final<-glm(income.num~age+years_education+marital_status+occupation+household_role+gender+race+capital_loss_log+capital_gain_log, data=training_set2, family="binomial")
testing_set3<-cbind(testing_set2, capital_data_testing)
predicted_probs_final <- predict(glm_final, newdata = testing_set3, type = "response")
testing_set3$income.num <- ifelse(testing_set3$income == "<=50K", 1, 0)
predicted_classes_final <- ifelse(predicted_probs_final < optimal_threshold_final$threshold,1, 0)
result_df <- data.frame(Index = as.numeric(names(predicted_classes_final)),
Predicted_Class = as.factor(predicted_classes_final))
confusion_matrix_final_reduced_with_outliers <- confusionMatrix(result_df$Predicted_Class, factor(testing_set3$income.num))
confusion_matrix_final_reduced_with_outliers## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1335 984
## 1 265 3928
##
## Accuracy : 0.8082
## 95% CI : (0.7984, 0.8177)
## No Information Rate : 0.7543
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5506
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.8344
## Specificity : 0.7997
## Pos Pred Value : 0.5757
## Neg Pred Value : 0.9368
## Prevalence : 0.2457
## Detection Rate : 0.2050
## Detection Prevalence : 0.3561
## Balanced Accuracy : 0.8170
##
## 'Positive' Class : 0
## Reduced Model Without Outliers
set.seed(1234)
outlier_indices <- c(4569, 5386, 10459, 15377, 17040, 18437, 20177, 31369, 32215, 32562)
training_set_outliers_removed <- training_set2[-outlier_indices, ]
outlier_indices <- c(4569, 5386, 10459, 15377, 17040, 18437, 20177, 31369, 32215, 32562)
testing_set_outliers_removed <- testing_set3[-outlier_indices, ]
glm_final_reduced_without_outliers<-glm(income.num~age+years_education+marital_status+occupation+household_role+gender+race+capital_loss_log+capital_gain_log, data=training_set_outliers_removed, family="binomial")ROC: Reduced Model Without Outliers
# AUROC
glm_final<-glm(income.num~age+years_education+marital_status+occupation+household_role+gender+race+capital_loss_log+capital_gain_log, data=training_set_outliers_removed, family="binomial")
predicted_probs_final <- predict(glm_final, newdata = testing_set_outliers_removed, type = "response")
testing_set_outliers_removed$income.num <- ifelse(testing_set_outliers_removed$income == ">50K", 1, 0)
roc_curve_final <- roc(testing_set_outliers_removed$income, predicted_probs_final)## Setting levels: control = <=50K, case = >50K## Setting direction: controls < casesoptimal_threshold_final<- coords(roc_curve_final, "best", best.method = "closest.topleft")
plot(roc_curve_final, main = "ROC Curve of Reduced Model Without Outliers", col = "blue")
abline(a = 0, b = 1, lty = 2, col = "red")
legend("bottomright", legend = paste("AUC =", round(auc(roc_curve_final), 5)), col = "blue", lwd = 2)
Metrics: Reduced Model Without Outliers
set.seed(1234)
glm_final<-glm(income.num~age+years_education+marital_status+occupation+household_role+gender+race+log_capital_loss+log_capital_gain, data=training_set_outliers_removed, family="binomial")
predicted_probs_final <- predict(glm_final, newdata = testing_set_outliers_removed, type = "response")
testing_set_outliers_removed$income.num <- ifelse(testing_set_outliers_removed$income == "<=50K", 1, 0)
predicted_classes_final <- ifelse(predicted_probs_final < optimal_threshold_final$threshold,1, 0)
result_df <- data.frame(Index = as.numeric(names(predicted_classes_final)),
Predicted_Class = as.factor(predicted_classes_final))
confusion_matrix_reduced_without_outliers <- confusionMatrix(result_df$Predicted_Class, factor(testing_set_outliers_removed$income.num))
confusion_matrix_reduced_without_outliers## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1354 1033
## 1 245 3878
##
## Accuracy : 0.8037
## 95% CI : (0.7938, 0.8133)
## No Information Rate : 0.7544
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5457
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.8468
## Specificity : 0.7897
## Pos Pred Value : 0.5672
## Neg Pred Value : 0.9406
## Prevalence : 0.2456
## Detection Rate : 0.2080
## Detection Prevalence : 0.3667
## Balanced Accuracy : 0.8182
##
## 'Positive' Class : 0
## Minimal change in performance with removal of outliers. We will move forward with the reduced model including the outliers
Model Interpretation: Reduced Model (With Outliers)
glm_final<-glm(income.num~age+years_education+marital_status+occupation+household_role+gender+race+capital_loss_log+capital_gain_log, data=training_set2, family="binomial")
summary(glm_final)##
## Call:
## glm(formula = income.num ~ age + years_education + marital_status +
## occupation + household_role + gender + race + capital_loss_log +
## capital_gain_log, family = "binomial", data = training_set2)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.767626 0.404255 -19.215 < 2e-16 ***
## age 0.020839 0.001678 12.417 < 2e-16 ***
## years_education 0.290603 0.009251 31.413 < 2e-16 ***
## marital_statusmarried 0.535167 0.177654 3.012 0.00259 **
## marital_statusnotmarried -0.468821 0.087211 -5.376 7.63e-08 ***
## marital_statuswidowed 0.027897 0.160841 0.173 0.86230
## occupationclerical 0.847963 0.141608 5.988 2.12e-09 ***
## occupationhighskill 1.478613 0.127666 11.582 < 2e-16 ***
## occupationlowskill 0.006958 0.150122 0.046 0.96303
## occupationmidskill 0.856592 0.129186 6.631 3.34e-11 ***
## occupationother 0.012684 0.162331 0.078 0.93772
## household_rolewife 1.216307 0.107220 11.344 < 2e-16 ***
## household_roletenant -1.013876 0.173166 -5.855 4.77e-09 ***
## household_rolesingle_occ -1.263422 0.194789 -6.486 8.81e-11 ***
## household_rolerelative -1.526758 0.240548 -6.347 2.20e-10 ***
## household_roledepchild -2.173666 0.214978 -10.111 < 2e-16 ***
## gendermale 0.927797 0.081051 11.447 < 2e-16 ***
## racewhite 0.841636 0.306211 2.749 0.00599 **
## raceblack 0.720119 0.314635 2.289 0.02209 *
## racenative 0.431459 0.384835 1.121 0.26222
## raceAsian_Pac_Islander 0.619243 0.323642 1.913 0.05570 .
## capital_loss_log 0.151753 0.010328 14.694 < 2e-16 ***
## capital_gain_log 0.205507 0.007069 29.074 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28686 on 26049 degrees of freedom
## Residual deviance: 17745 on 26027 degrees of freedom
## AIC: 17791
##
## Number of Fisher Scoring iterations: 7set.seed(1234)
coef(glm_final)## (Intercept) age years_education
## -7.767626213 0.020838881 0.290602682
## marital_statusmarried marital_statusnotmarried marital_statuswidowed
## 0.535166588 -0.468820538 0.027897488
## occupationclerical occupationhighskill occupationlowskill
## 0.847962934 1.478613174 0.006957833
## occupationmidskill occupationother household_rolewife
## 0.856592004 0.012683882 1.216306810
## household_roletenant household_rolesingle_occ household_rolerelative
## -1.013875798 -1.263422200 -1.526757787
## household_roledepchild gendermale racewhite
## -2.173665697 0.927796823 0.841635881
## raceblack racenative raceAsian_Pac_Islander
## 0.720118918 0.431458648 0.619242942
## capital_loss_log capital_gain_log
## 0.151752515 0.205507364exp(cbind("Coefficient" = coef(glm_final), confint.default(glm_final, level=0.95)))## Coefficient 2.5 % 97.5 %
## (Intercept) 0.0004232167 0.0001916287 0.0009346846
## age 1.0210575264 1.0177044157 1.0244216849
## years_education 1.3372331713 1.3132051851 1.3617008025
## marital_statusmarried 1.7077327069 1.2055933165 2.4190172245
## marital_statusnotmarried 0.6257398697 0.5274234429 0.7423833539
## marital_statuswidowed 1.0282902672 0.7502532455 1.4093652777
## occupationclerical 2.3348856886 1.7690046602 3.0817845208
## occupationhighskill 4.3868576525 3.4157333896 5.6340814308
## occupationlowskill 1.0069820950 0.7503048189 1.3514679824
## occupationmidskill 2.3551207590 1.8283111190 3.0337253500
## occupationother 1.0127646639 0.7367707720 1.3921457031
## household_rolewife 3.3747012790 2.7350779205 4.1639064967
## household_roletenant 0.3628100725 0.2583928778 0.5094225113
## household_rolesingle_occ 0.2826849648 0.1929736107 0.4141021616
## household_rolerelative 0.2172388614 0.1355760567 0.3480903933
## household_roledepchild 0.1137598426 0.0746448075 0.1733717618
## gendermale 2.5289313521 2.1574772372 2.9643389387
## racewhite 2.3201593787 1.2731246302 4.2282895284
## raceblack 2.0546775334 1.1089854353 3.8068126343
## racenative 1.5395014780 0.7241161487 3.2730450837
## raceAsian_Pac_Islander 1.8575212580 0.9850294224 3.5028245305
## capital_loss_log 1.1638721595 1.1405503474 1.1876708527
## capital_gain_log 1.2281480256 1.2112504824 1.2452812979Intercept (Beta_0): Using a chi-square test at the 95% confidence level, the coefficient of the intercept (Beta_0) is estimated at -7.498387, with a p-value of < 2e-16. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_0 is equal to 0. The confidence interval (CI) for Beta_0 is (-8.020812970, -6.97596079).
Age (Beta_1): Using a chi-square test at the 95% confidence level, the coefficient of the age variable (Beta_1) is estimated at 0.016518, with a final p-value of < 2e-16. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_1 is equal to 0.
With these results, there is evidence at the 95% confidence level that a 10-unit change in age corresponds to a 1.179607 factor increase in the odds of being high income by approximately 1.02, with a confidence interval between (1.1424937, 1.2179269).
Years of Education (Beta_2): Using a chi-square test at the 95% confidence level, the coefficient of the years_education variable (Beta_2) is estimated at 0.308309, with a final p-value of < 2e-16. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_2 is equal to 0.
With these results, there is evidence at the 95% confidence level that a 10-unit change in years of education corresponds to a substantial increase in the odds of being high income, with a 21.825787 factor increase of approximately 1.36, and a confidence interval between (18.25955, 26.08852).
Marital Status: Married (Beta_3): Using a chi-square test at the 95% confidence level, the coefficient of the marital_statusmarried variable (Beta_3) is estimated at 0.496526, with a final p-value of 0.00430. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_3 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a married individual making >50K are 1.643003 times greater than individuals who are divorced, with a confidence interval between (1.168419, 2.310352).
Not Married (Beta_4): Using a chi-square test at the 95% confidence level, the coefficient of the marital_statusnotmarried variable (Beta_4) is estimated at -0.586497, with a final p-value of 6.80e-12. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_4 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of an unmarried individual making >50K are 5.562727e-01 times lower than individuals who are divorced, with a confidence interval between (.4704704, .6577234).
Widowed (Beta_5): Using a chi-square test at the 95% confidence level, the coefficient of the marital_statuswidowed variable (Beta_5) is estimated at -0.194806, with a final p-value of 0.22970. Given the p-value is above our alpha threshold of 0.05, there is not sufficient evidence to reject the null hypothesis that Beta_5 is equal to 0.
Occupation: Clerical (Beta_6): Using a chi-square test at the 95% confidence level, the coefficient of the occupationclerical variable (Beta_6) is estimated at 0.908345, with a final p-value of 1.61e-10. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_6 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a clerical worker making >50K are 2.480215 times higher than those of an agricultural worker, with a confidence interval between (1.877497e+00, 3.276418e+00).
High Skill (Beta_7): Using a chi-square test at the 95% confidence level, the coefficient of the occupationhighskill variable (Beta_7) is estimated at 1.352085, with a final p-value of < 2e-16. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_7 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a high-skill worker making >50K are 3.865477 times higher than those of an agricultural worker, with a confidence interval between (3.010316, 4.963568).
Low Skill (Beta_8): Using a chi-square test at the 95% confidence level, the coefficient of the occupationlowskill variable (Beta_8) is estimated at -0.053504, with a final p-value of 0.72441. Given the p-value is above our alpha threshold of 0.05, there is not sufficient evidence to reject the null hypothesis that Beta_8 is equal to 0.
Mid Skill (Beta_9): Using a chi-square test at the 95% confidence level, the coefficient of the occupationmidskill variable (Beta_9) is estimated at 0.857840, with a final p-value of 3.55e-11. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_9 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a mid-skill worker making >50K are 2.358061 times higher than those of an agricultural worker, with a confidence interval between (1.829278, 3.039697).
Household Role: Husband (Beta_10): Using a chi-square test at the 95% confidence level, the coefficient of the household_rolehusband variable (Beta_10) is estimated at 2.275175, with a final p-value of < 2e-16. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_10 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a husband making >50K are 9.729618 times higher than those of a dependent, with a confidence interval between (6.434549, 14.71206).
Relative (Beta_11): Using a chi-square test at the 95% confidence level, the coefficient of the household_rolerelative variable (Beta_11) is estimated at 0.697999, with a final p-value of 0.00605. Given the p-value is below our alpha threshold of 0.05, there is sufficient evidence to reject the null hypothesis that Beta_11 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a relative within a family household making >50K are 2.009726 times higher than those of a dependent, with a confidence interval between (1.220959e+00, 3.308055e+00).
Single Occupant (Beta_12): Using a chi-square test at the 95% confidence level, the coefficient of the household_rolesingle_occ variable (Beta_12) is estimated at 0.989746, with a final p-value of 6.94e-09. Given the p-value is below our alpha threshold of 0.05, there is sufficient evidence to reject the null hypothesis that Beta_12 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a single-occupant making >50K are 27.83640 times higher than those of a dependent, with a confidence interval between (1.924844e+00, 3.760856e+00).
Tenant (Beta_13): Using a chi-square test at the 95% confidence level, the coefficient of the household_roletenant variable (Beta_13) is estimated at 1.271726, with a final p-value of < 2e-16. Given the p-value is below our alpha threshold of 0.05, there is sufficient evidence to reject the null hypothesis that Beta_13 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a tenant making >50K are 3.567003 times higher than those of a dependent, with a confidence interval between (2.679115e+00, 4.749146e+00).
Wife (Beta_14): Using a chi-square test at the 95% confidence level, the coefficient of the household_rolewife variable (Beta_14) is estimated at 3.32634446, with a final p-value of < 2e-16. Given the p-value is below our alpha threshold of 0.05, there is sufficient evidence to reject the null hypothesis that Beta_14 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a wife within the household making >50K are 27.84856 timeshigher than those of a dependent, with a confidence interval between (17.92502, 43.22814).
Gender: Male (Beta_15): Using a chi-square test at the 95% confidence level, the coefficient of the gendermale variable (Beta_15) is estimated at 0.870530, with a final p-value of < 2e-16. Given the p-value is below our alpha threshold of 0.05, there is sufficient evidence to reject the null hypothesis that Beta_15 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a male making >50K are 2.388176 times higher than those of a female, with a confidence interval between (2.043648, 2.790788).
Race: Black (Beta_16): Using a chi-square test at the 95% confidence level, the coefficient of the raceblack variable (Beta_16) is estimated at 0.081020, with a final p-value of 0.53106. Given the p-value is above our alpha threshold of 0.05, there is not sufficient evidence to reject the null hypothesis that Beta_16 is equal to 0.
Native (Beta_17): Using a chi-square test at the 95% confidence level, the coefficient of the racenative variable (Beta_17) is estimated at -0.218989, with a final p-value of 0.38926. Given the p-value is above our alpha threshold of 0.05, there is not sufficient evidence to reject the null hypothesis that Beta_17 is equal to 0.
White (Beta_18): Using a chi-square test at the 95% confidence level, the coefficient of the racewhite variable (Beta_18) is estimated at 0.218540, with a final p-value of 0.04187. Given the p-value is below our alpha threshold of 0.05, there is some evidence to reject the null hypothesis that Beta_18 is equal to 0.
With these results, there is evidence at the 95% confidence level that the odds of a white individual making >50K are 1.244259 times higher than those of an asian individual, with a confidence interval between (1.008067, 1.535791).
Logged Capital Loss (Beta_19): Using a chi-square test at the 95% confidence level, the coefficient of the log_capital_loss variable (Beta_19) is estimated at 0.051903, with a final p-value of < 2e-16. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_19 is equal to 0.
With these results, there is evidence at the 95% confidence level that a 10-unit change in log_capital_loss corresponds to a 1.68040 factor increase in the odds of being high income, with a confidence interval between (1.045891, 1.060709).
Logged Capital Gain (Beta_20): Using a chi-square test at the 95% confidence level, the coefficient of the log_capital_gain variable (Beta_20) is estimated at 0.077103, with a final p-value of < 2e-16. Given the p-value is below our alpha threshold of 0.05, there is strong evidence to reject the null hypothesis that Beta_20 is equal to 0.
With these results, there is evidence at the 95% confidence level that a 10-unit change in log_capital_gain corresponds to a 2.161985 factor increase in the odds of being high income, with a confidence interval between (2.050229, 2.279832)
Objective 2: Predictive Models
Steven’s function for statistics
Generic Function to get all the statistics
# Example:
# print_model_metrics(model = generic_model, test_data = testing_set, response_var = "income", pos_value = "Over.50K", threshold = 0.28, model_title = "Simple GLM")
#
library(caret)
library(pROC)
print_model_metrics <-
function(model,
test_data,
response_var,
pos_value,
threshold = 0.5,
model_title = "Model Metrics") {
# Ensure that the response variable is a factor with exactly two levels
response_factor <-
factor(test_data[[response_var]], levels = unique(test_data[[response_var]]))
# Make sure we have exactly two levels
if (length(levels(response_factor)) != 2) {
stop("Response variable must have exactly two levels.")
}
# Make predictions and convert to factor with the specified positive value
probabilities <- predict(model, test_data, type = "response")
predicted_classes <-
factor(ifelse(
probabilities > threshold,
levels(response_factor)[2],
levels(response_factor)[1]
),
levels = levels(response_factor))
# Calculate the confusion matrix using caret, using pos_value to specify the positive class
cm <-
confusionMatrix(predicted_classes, response_factor, positive = pos_value)
# Extract metrics from the confusion matrix
accuracy <- cm$overall['Accuracy']
sensitivity <- cm$byClass['Sensitivity']
specificity <- cm$byClass['Specificity']
prevalence <- cm$byClass['Prevalence']
ppv <- cm$byClass['Pos Pred Value']
npv <- cm$byClass['Neg Pred Value']
# Calculate AUROC using pROC, ensuring numeric conversion accounts for positive class being '1'
roc_obj <-
roc(response = as.numeric(response_factor) - 1,
predictor = probabilities)
auroc <- auc(roc_obj)
# Replace NA values with zero or another appropriate placeholder
metrics_values <-
c(accuracy,
sensitivity,
specificity,
prevalence,
ppv,
npv,
auroc)
metrics_values[is.na(metrics_values)] <- 0
# Compile the metrics into a data frame
metrics <- data.frame(
Metric = c(
"Accuracy",
"Sensitivity",
"Specificity",
"Prevalence",
"PPV",
"NPV",
"AUROC"
),
Value = metrics_values
)
# Prepare the caption with the threshold information
aic_val <- AIC(model)
full_caption <-
sprintf("%s\nThreshold: %s\nAIC: %.2f",
model_title,
threshold,
aic_val)
# Print the metrics table using knitr
knitr::kable(
metrics,
col.names = c("Metric", "Value"),
format = "markdown",
caption = full_caption,
)
}Steven’s function call
print_model_metrics(model = full.model, test_data = testing_set_out, response_var = "income", pos_value = ">50K", threshold = 0.26, model_title = "Simple GLM")## Setting levels: control = 0, case = 1## Setting direction: controls < cases| Metric | Value | |
|---|---|---|
| Accuracy | Accuracy | 0.8157935 |
| Sensitivity | Sensitivity | 0.8304562 |
| Specificity | Specificity | 0.8109269 |
| Prevalence | Prevalence | 0.2491934 |
| Pos Pred Value | PPV | 0.5931308 |
| Neg Pred Value | NPV | 0.9351109 |
| AUROC | 0.9049136 |
Steven’s plot function
Generic Function to plot metrix by threshold
set.seed(1234)
library(caret)
library(pROC)
library(ggplot2)
# Example
# plot_model_metrics_by_threshold(model = {your_model}, test_data = {your_test_set}, response_var = "income", pos_value = {"Greater.50K"})
plot_model_metrics_by_threshold <-
function(model,
test_data,
response_var,
pos_value,
thresholds = seq(0.1, 0.9, by = 0.05),
model_title = "Model Metrics by Threshold") {
# Ensure the response variable is a factor with exactly two levels
response_factor <-
factor(test_data[[response_var]], levels = unique(test_data[[response_var]]))
# Make sure we have exactly two levels
if (length(levels(response_factor)) != 2) {
stop("Response variable must have exactly two levels.")
}
# Prepare to collect metrics
results <-
data.frame(
Threshold = numeric(0),
Accuracy = numeric(0),
Sensitivity = numeric(0),
Specificity = numeric(0),
Prevalence = numeric(0),
PPV = numeric(0),
NPV = numeric(0)
)
# Loop over each threshold
for (threshold in thresholds) {
# Make predictions and convert to factor with the specified positive value
probabilities <- predict(model, test_data, type = "response")
predicted_classes <-
factor(
ifelse(
probabilities > threshold,
pos_value,
levels(response_factor)[levels(response_factor) != pos_value]
),
levels = levels(response_factor)
)
# Calculate the confusion matrix using caret
cm <-
confusionMatrix(predicted_classes, response_factor, positive = pos_value)
# Store metrics
results <- rbind(
results,
data.frame(
Threshold = threshold,
Accuracy = cm$overall['Accuracy'],
Sensitivity = cm$byClass['Sensitivity'],
Specificity = cm$byClass['Specificity'],
Prevalence <- cm$byClass['Prevalence'],
PPV = cm$byClass['Pos Pred Value'],
NPV = cm$byClass['Neg Pred Value']
)
)
}
# Melt results for plotting
results_long <-
reshape2::melt(results, id.vars = 'Threshold', variable.name = 'Metric')
# Plot results
plot <-
ggplot(results_long, aes(x = Threshold, y = value, color = Metric)) +
geom_line() +
labs(title = model_title, x = "Threshold", y = "Metric Value") +
scale_color_manual(
values = c(
"Accuracy" = "blue",
"Sensitivity" = "red",
"Specificity" = "green",
"Prevalence" = "purple",
"PPV" = "orange",
"NPV" = "brown"
)
) +
scale_y_continuous(limits = c(0, 1)) +
ggtitle(paste("Metrics for Various Thresholds -", model_title))
# Return the plot
return(plot)
}plot_model_metrics_by_threshold(model =full.model, test_data = testing_set_out, response_var = "income", pos_value = ">50K")
Complex Models with Interactions and Polynomials
Complex: Poly Squared
\[ \hat{Y}_{\text{income}} = \beta_0 \\ + \beta_{1} \text{marital_status} \\ + \beta_{2} \text{household_role} \\ + \beta_{3} \text{occupation}\\ + \beta_4 \text{years_education} \\ + \beta_5 \text{age} + \beta_6 \text{age}^2 \\ + \beta_7 \text{hours_per_week} + \beta_8 \text{hours_per_week}^2 \\ + \beta_9 \text{capital_gain_log} + \beta_{10} \text{capital_gain_log}^2 \\ + \beta_{11} \text{capital_loss_log} + \beta_{12} \text{capital_loss_log}^2 \\ \]
model_formula <- as.formula("income ~ poly(age,2) +
poly(years_education,2) +
poly(hours_per_week,2) +
poly(capital_gain_log,2) +
poly(capital_loss_log,2) +
marital_status_num +
household_role_num +
occupation_num")
poly.fit <- glm(model_formula, data = training_set_LR, family = "binomial")
summary(poly.fit)##
## Call:
## glm(formula = model_formula, family = "binomial", data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.62506 0.11051 5.656 1.55e-08 ***
## poly(age, 2)1 79.98704 4.24033 18.863 < 2e-16 ***
## poly(age, 2)2 -87.09251 4.82640 -18.045 < 2e-16 ***
## poly(years_education, 2)1 131.15633 4.05644 32.333 < 2e-16 ***
## poly(years_education, 2)2 6.01310 4.10787 1.464 0.1432
## poly(hours_per_week, 2)1 55.43791 4.08927 13.557 < 2e-16 ***
## poly(hours_per_week, 2)2 -28.45053 3.66390 -7.765 8.16e-15 ***
## poly(capital_gain_log, 2)1 165.11045 6.54476 25.228 < 2e-16 ***
## poly(capital_gain_log, 2)2 224.08600 11.06536 20.251 < 2e-16 ***
## poly(capital_loss_log, 2)1 35.54709 2.71729 13.082 < 2e-16 ***
## poly(capital_loss_log, 2)2 31.52636 3.70030 8.520 < 2e-16 ***
## marital_status_num -0.06609 0.03649 -1.811 0.0701 .
## household_role_num -0.84908 0.01931 -43.974 < 2e-16 ***
## occupation_num -0.08631 0.01728 -4.994 5.91e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 17095 on 26036 degrees of freedom
## AIC: 17123
##
## Number of Fisher Scoring iterations: 7par(mfrow=c(2,2))
plot(poly.fit)
par(mfrow=c(1,1))
plot_model_metrics_by_threshold(model = poly.fit, test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
model_title = "Simple Fit with Polynomial ^2")
poly2 <- print_model_metrics(model = poly.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
threshold = 0.267,
model_title = "Simple Fit with Polynomial ^2")## Setting levels: control = 0, case = 1## Setting direction: controls < casesprint(poly2)##
##
## Table: Simple Fit with Polynomial ^2
## Threshold: 0.267
## AIC: 17122.78
##
## | |Metric | Value|
## |:--------------|:-----------|---------:|
## |Accuracy |Accuracy | 0.8069717|
## |Sensitivity |Sensitivity | 0.8289141|
## |Specificity |Specificity | 0.7999188|
## |Prevalence |Prevalence | 0.2432432|
## |Pos Pred Value |PPV | 0.5711179|
## |Neg Pred Value |NPV | 0.9356753|
## | |AUROC | 0.9020507|Complex: Poly Cubed
\[ \hat{Y}_{\text{income}} = \beta_0 \\ + \beta_{1} \text{marital_status} \\ + \beta_{2} \text{household_role} \\ + \beta_{3} \text{occupation}\\ + \beta_4 \text{years_education} \\ + \beta_5 \text{age} + \beta_6 \text{age}^3 \\ + \beta_7 \text{hours_per_week} + \beta_8 \text{hours_per_week}^3 \\ + \beta_9 \text{capital_gain_log} + \beta_{10} \text{capital_gain_log}^3 \\ + \beta_{11} \text{capital_loss_log} + \beta_{12} \text{capital_loss_log} \\ \]
model_formula3 <- as.formula("income ~ poly(age,3) +
poly(years_education,3) +
poly(hours_per_week,3) +
poly(capital_gain_log,3) +
capital_loss_log +
marital_status_num +
household_role_num +
occupation_num")
poly3.fit <- glm(model_formula3, data = training_set_LR, family = "binomial")
summary(poly3.fit)##
## Call:
## glm(formula = model_formula3, family = "binomial", data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.39918 0.11197 3.565 0.000364 ***
## poly(age, 3)1 96.98297 5.52301 17.560 < 2e-16 ***
## poly(age, 3)2 -92.87314 5.01600 -18.515 < 2e-16 ***
## poly(age, 3)3 23.72988 4.76139 4.984 6.23e-07 ***
## poly(years_education, 3)1 128.28773 4.06701 31.543 < 2e-16 ***
## poly(years_education, 3)2 7.22034 4.27434 1.689 0.091175 .
## poly(years_education, 3)3 -0.18318 3.64841 -0.050 0.959957
## poly(hours_per_week, 3)1 52.10159 4.06692 12.811 < 2e-16 ***
## poly(hours_per_week, 3)2 -24.17059 3.75039 -6.445 1.16e-10 ***
## poly(hours_per_week, 3)3 -10.93451 3.25761 -3.357 0.000789 ***
## poly(capital_gain_log, 3)1 110.39230 7.81136 14.132 < 2e-16 ***
## poly(capital_gain_log, 3)2 140.51804 11.78670 11.922 < 2e-16 ***
## poly(capital_gain_log, 3)3 -86.68426 12.26486 -7.068 1.58e-12 ***
## capital_loss_log 0.14847 0.01010 14.707 < 2e-16 ***
## marital_status_num -0.05236 0.03623 -1.445 0.148428
## household_role_num -0.83787 0.01923 -43.564 < 2e-16 ***
## occupation_num -0.08674 0.01732 -5.007 5.52e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 17116 on 26033 degrees of freedom
## AIC: 17150
##
## Number of Fisher Scoring iterations: 7par(mfrow=c(2,2))
plot(poly3.fit)
par(mfrow=c(1,1))
plot_model_metrics_by_threshold(
model = poly3.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
model_title = "Simple Fit with Polynomial ^3"
)
poly3 <-
print_model_metrics(
model = poly3.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
threshold = 0.267,
model_title = "Simple Fit with Polynomial Cubed"
)## Setting levels: control = 0, case = 1## Setting direction: controls < casesprint(poly3)##
##
## Table: Simple Fit with Polynomial Cubed
## Threshold: 0.267
## AIC: 17150.40
##
## | |Metric | Value|
## |:--------------|:-----------|---------:|
## |Accuracy |Accuracy | 0.8029791|
## |Sensitivity |Sensitivity | 0.8270202|
## |Specificity |Specificity | 0.7952516|
## |Prevalence |Prevalence | 0.2432432|
## |Pos Pred Value |PPV | 0.5648987|
## |Neg Pred Value |NPV | 0.9346530|
## | |AUROC | 0.9021602|Interactions between AgeGender and Hours_per_Week Race
\[ \hat{Y}_{\text{income}} = \beta_0 \\ + \beta_1 \text{age} + \beta_2 \text{gender} + \beta_3 \text{hours_per_week}\\ + \beta_4 \text{years_education}+ \beta_5 \text{marital_status} \\ + \beta_6 \text{capital_gain_log} + \beta_{7} \text{capital_loss_log}\\ + \beta_{8} \text{household_role} + \beta_{9} \text{occupation} \\ + \beta_{10} (\text{age} \times \text{gender}) + \beta_{11} (\text{years_education} \\ + \beta_{12} (\text{hours_per_week} \times \text{race}) \\ \]
model_formula <- as.formula("income ~ age * gender_num +
hours_per_week * race_num +
years_education +
capital_gain_log +
capital_loss_log +
household_role_num +
marital_status_num +
occupation_num")
complex.fit <- glm(model_formula, data = training_set_LR, family = "binomial")
summary(complex.fit)##
## Call:
## glm(formula = model_formula, family = "binomial", data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.7286439 0.4231894 -8.811 < 2e-16 ***
## age 0.0190514 0.0065386 2.914 0.00357 **
## gender_num -0.1338344 0.1588156 -0.843 0.39939
## hours_per_week 0.0193465 0.0060982 3.173 0.00151 **
## race_num -0.2106693 0.1213555 -1.736 0.08257 .
## years_education 0.3363655 0.0084214 39.942 < 2e-16 ***
## capital_gain_log 0.1992702 0.0068839 28.947 < 2e-16 ***
## capital_loss_log 0.1513372 0.0099559 15.201 < 2e-16 ***
## household_role_num -0.8588589 0.0222545 -38.593 < 2e-16 ***
## marital_status_num -0.2104452 0.0332061 -6.338 2.33e-10 ***
## occupation_num -0.0916370 0.0166034 -5.519 3.41e-08 ***
## age:gender_num 0.0007675 0.0035957 0.213 0.83097
## hours_per_week:race_num 0.0037267 0.0027283 1.366 0.17196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 18587 on 26037 degrees of freedom
## AIC: 18613
##
## Number of Fisher Scoring iterations: 6formula(complex.fit)## income ~ age * gender_num + hours_per_week * race_num + years_education +
## capital_gain_log + capital_loss_log + household_role_num +
## marital_status_num + occupation_numpar(mfrow=c(2,2))
plot(complex.fit)
par(mfrow=c(1,1))
plot_model_metrics_by_threshold(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
model_title = "Complex (Interactions)"
)
compleX <-
print_model_metrics(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
threshold = 0.267,
model_title = "Interactions:(Hours_per_week* Race)"
)## Setting levels: control = 0, case = 1## Setting direction: controls < casesprint(complex)## function (length.out = 0L, real = numeric(), imaginary = numeric(),
## modulus = 1, argument = 0)
## {
## if (missing(modulus) && missing(argument)) {
## .Internal(complex(length.out, real, imaginary))
## }
## else {
## n <- max(length.out, length(argument), length(modulus))
## rep_len(modulus, n) * exp((0+1i) * rep_len(argument,
## n))
## }
## }
## <bytecode: 0x000001fb9cc27e40>
## <environment: namespace:base>Complex: Poly numericals with interactions age*gender, marital_status(hour and years education)
model_formula <- as.formula("income ~ poly(age,2)*gender_num +
poly(years_education,2) * marital_status_num +
poly(hours_per_week,2) * marital_status_num +
poly(capital_gain_log,2) +
poly(capital_loss_log,2) +
household_role_num +
occupation_num")
complex.fit <- glm(model_formula, data = training_set_LR, family = "binomial")
summary(complex.fit)##
## Call:
## glm(formula = model_formula, family = "binomial", data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 0.79702 0.18918 4.213
## poly(age, 2)1 36.17184 18.62002 1.943
## poly(age, 2)2 -123.78007 23.82016 -5.196
## gender_num -0.14252 0.07060 -2.019
## poly(years_education, 2)1 179.90712 16.60446 10.835
## poly(years_education, 2)2 -30.73809 16.91262 -1.817
## marital_status_num -0.01297 0.04410 -0.294
## poly(hours_per_week, 2)1 52.85092 16.18969 3.264
## poly(hours_per_week, 2)2 -3.28142 15.98982 -0.205
## poly(capital_gain_log, 2)1 165.13669 6.53199 25.281
## poly(capital_gain_log, 2)2 224.22126 11.03726 20.315
## poly(capital_loss_log, 2)1 35.49121 2.72563 13.021
## poly(capital_loss_log, 2)2 32.97319 3.79609 8.686
## household_role_num -0.88242 0.02458 -35.905
## occupation_num -0.07990 0.01745 -4.578
## poly(age, 2)1:gender_num 23.96808 10.16080 2.359
## poly(age, 2)2:gender_num 19.56867 12.69974 1.541
## poly(years_education, 2)1:marital_status_num -23.83973 7.71879 -3.089
## poly(years_education, 2)2:marital_status_num 17.85435 7.81782 2.284
## marital_status_num:poly(hours_per_week, 2)1 2.21349 7.73001 0.286
## marital_status_num:poly(hours_per_week, 2)2 -12.75057 7.71690 -1.652
## Pr(>|z|)
## (Intercept) 2.52e-05 ***
## poly(age, 2)1 0.05206 .
## poly(age, 2)2 2.03e-07 ***
## gender_num 0.04351 *
## poly(years_education, 2)1 < 2e-16 ***
## poly(years_education, 2)2 0.06915 .
## marital_status_num 0.76862
## poly(hours_per_week, 2)1 0.00110 **
## poly(hours_per_week, 2)2 0.83740
## poly(capital_gain_log, 2)1 < 2e-16 ***
## poly(capital_gain_log, 2)2 < 2e-16 ***
## poly(capital_loss_log, 2)1 < 2e-16 ***
## poly(capital_loss_log, 2)2 < 2e-16 ***
## household_role_num < 2e-16 ***
## occupation_num 4.70e-06 ***
## poly(age, 2)1:gender_num 0.01833 *
## poly(age, 2)2:gender_num 0.12335
## poly(years_education, 2)1:marital_status_num 0.00201 **
## poly(years_education, 2)2:marital_status_num 0.02238 *
## marital_status_num:poly(hours_per_week, 2)1 0.77461
## marital_status_num:poly(hours_per_week, 2)2 0.09848 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 17068 on 26029 degrees of freedom
## AIC: 17110
##
## Number of Fisher Scoring iterations: 7par(mfrow=c(2,2))
plot(complex.fit)
par(mfrow=c(1,1))
plot_model_metrics_by_threshold(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
model_title = "Complex (Interactions with poly)"
)
poly.mixed <-
print_model_metrics(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
threshold = 0.267,
model_title = "Complex (poly w/interactions)"
)## Setting levels: control = 0, case = 1## Setting direction: controls < casesprint(poly.mixed)##
##
## Table: Complex (poly w/interactions)
## Threshold: 0.267
## AIC: 17110.38
##
## | |Metric | Value|
## |:--------------|:-----------|---------:|
## |Accuracy |Accuracy | 0.8085074|
## |Sensitivity |Sensitivity | 0.8333333|
## |Specificity |Specificity | 0.8005276|
## |Prevalence |Prevalence | 0.2432432|
## |Pos Pred Value |PPV | 0.5731654|
## |Neg Pred Value |NPV | 0.9372773|
## | |AUROC | 0.9028149|Complex Model interaction Native Region
# Using Native Country as interaction except for capital_gain_log and capital_loss_log which is using invested
model_formula <- as.formula("income ~ age * native_region_num +
years_education * native_region_num +
hours_per_week * native_region_num +
capital_gain_log * native_region_num +
capital_loss_log * native_region_num +
marital_status_num +
household_role_num +
occupation_num")
complex.fit <- glm(model_formula, data = training_set_LR, family = "binomial")
summary(complex.fit)##
## Call:
## glm(formula = model_formula, family = "binomial", data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.0813249 0.8060151 -3.823 0.000132 ***
## age 0.0216133 0.0100279 2.155 0.031138 *
## native_region_num -0.3522223 0.2034748 -1.731 0.083445 .
## years_education 0.2404679 0.0414374 5.803 6.51e-09 ***
## hours_per_week 0.0174820 0.0101493 1.722 0.084982 .
## capital_gain_log 0.1817072 0.0455101 3.993 6.53e-05 ***
## capital_loss_log 0.1237518 0.0618507 2.001 0.045412 *
## marital_status_num -0.2111590 0.0332197 -6.356 2.06e-10 ***
## household_role_num -0.8378151 0.0179476 -46.681 < 2e-16 ***
## occupation_num -0.0942614 0.0164810 -5.719 1.07e-08 ***
## age:native_region_num -0.0002785 0.0025458 -0.109 0.912896
## native_region_num:years_education 0.0250256 0.0106653 2.346 0.018953 *
## native_region_num:hours_per_week 0.0024466 0.0025856 0.946 0.344023
## native_region_num:capital_gain_log 0.0045493 0.0115818 0.393 0.694469
## native_region_num:capital_loss_log 0.0071856 0.0157401 0.457 0.648020
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 18587 on 26035 degrees of freedom
## AIC: 18617
##
## Number of Fisher Scoring iterations: 6par(mfrow=c(2,2))
plot(complex.fit)
par(mfrow=c(1,1))
plot_model_metrics_by_threshold(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
model_title = "Complex (Interaction Native Region)"
)
complex <-
print_model_metrics(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
threshold = 0.267,
model_title = "Complex (Interaction Occupation)"
)## Setting levels: control = 0, case = 1## Setting direction: controls < casesprint(complex)##
##
## Table: Complex (Interaction Occupation)
## Threshold: 0.267
## AIC: 18617.05
##
## | |Metric | Value|
## |:--------------|:-----------|---------:|
## |Accuracy |Accuracy | 0.7994472|
## |Sensitivity |Sensitivity | 0.8175505|
## |Specificity |Specificity | 0.7936282|
## |Prevalence |Prevalence | 0.2432432|
## |Pos Pred Value |PPV | 0.5601211|
## |Neg Pred Value |NPV | 0.9311905|
## | |AUROC | 0.8869810|Complex Model interaction Native Region with Poly
# Using Native Country as interaction except for capital_gain_log and capital_loss_log which is using invested
model_formula <- as.formula("income ~ age * native_region_num +
years_education * native_region_num +
marital_status_num +
household_role_num +
hours_per_week +
occupation_num +
poly(capital_gain_log,2) * native_region_num +
poly(capital_loss_log,2) * native_region_num")
complex.fit <- glm(model_formula, data = training_set_LR, family = "binomial")## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(complex.fit)##
## Call:
## glm(formula = model_formula, family = "binomial", data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -2.866e+00 7.022e-01 -4.082
## age 2.040e-02 1.048e-02 1.946
## native_region_num -2.714e-01 1.745e-01 -1.555
## years_education 2.431e-01 4.312e-02 5.639
## marital_status_num -2.257e-01 3.577e-02 -6.311
## household_role_num -8.742e-01 1.904e-02 -45.910
## hours_per_week 2.616e-02 1.662e-03 15.740
## occupation_num -8.900e-02 1.690e-02 -5.265
## poly(capital_gain_log, 2)1 2.861e+02 5.776e+01 4.953
## poly(capital_gain_log, 2)2 4.484e+02 9.475e+01 4.732
## poly(capital_loss_log, 2)1 2.687e+01 1.724e+01 1.559
## poly(capital_loss_log, 2)2 6.055e+01 1.983e+01 3.054
## age:native_region_num -5.952e-05 2.657e-03 -0.022
## native_region_num:years_education 2.229e-02 1.108e-02 2.012
## native_region_num:poly(capital_gain_log, 2)1 -3.125e+01 1.444e+01 -2.164
## native_region_num:poly(capital_gain_log, 2)2 -5.790e+01 2.374e+01 -2.439
## native_region_num:poly(capital_loss_log, 2)1 2.410e+00 4.371e+00 0.551
## native_region_num:poly(capital_loss_log, 2)2 -8.592e+00 4.969e+00 -1.729
## Pr(>|z|)
## (Intercept) 4.47e-05 ***
## age 0.05161 .
## native_region_num 0.11995
## years_education 1.71e-08 ***
## marital_status_num 2.78e-10 ***
## household_role_num < 2e-16 ***
## hours_per_week < 2e-16 ***
## occupation_num 1.40e-07 ***
## poly(capital_gain_log, 2)1 7.30e-07 ***
## poly(capital_gain_log, 2)2 2.22e-06 ***
## poly(capital_loss_log, 2)1 0.11906
## poly(capital_loss_log, 2)2 0.00226 **
## age:native_region_num 0.98213
## native_region_num:years_education 0.04420 *
## native_region_num:poly(capital_gain_log, 2)1 0.03044 *
## native_region_num:poly(capital_gain_log, 2)2 0.01471 *
## native_region_num:poly(capital_loss_log, 2)1 0.58140
## native_region_num:poly(capital_loss_log, 2)2 0.08382 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 17585 on 26032 degrees of freedom
## AIC: 17621
##
## Number of Fisher Scoring iterations: 7par(mfrow=c(2,2))
plot(complex.fit)
par(mfrow=c(1,1))
plot_model_metrics_by_threshold(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
model_title = "Complex (Interaction Native Region with poly^2)"
)
complex <-
print_model_metrics(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
threshold = 0.267,
model_title = "Complex (Interaction Native Region with poly^2)"
)## Setting levels: control = 0, case = 1## Setting direction: controls < casesprint(complex)##
##
## Table: Complex (Interaction Native Region with poly^2)
## Threshold: 0.267
## AIC: 17620.83
##
## | |Metric | Value|
## |:--------------|:-----------|---------:|
## |Accuracy |Accuracy | 0.8083538|
## |Sensitivity |Sensitivity | 0.8263889|
## |Specificity |Specificity | 0.8025568|
## |Prevalence |Prevalence | 0.2432432|
## |Pos Pred Value |PPV | 0.5736196|
## |Neg Pred Value |NPV | 0.9349882|
## | |AUROC | 0.8974720|Complex Model interaction Race
# Using Race as interaction except for capital_gain_log and capital_loss_log which is using invested
model_formula <- as.formula("income ~ age * race_num +
years_education * race_num +
marital_status_num * race_num +
household_role_num * race_num +
hours_per_week +
poly(capital_gain_log, 2)+
poly(capital_loss_log, 2)")
complex.fit <- glm(model_formula, data = training_set_LR, family = "binomial")
summary(complex.fit)##
## Call:
## glm(formula = model_formula, family = "binomial", data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.952107 0.549741 -9.008 < 2e-16 ***
## age 0.025775 0.006274 4.108 3.98e-05 ***
## race_num 0.239759 0.241713 0.992 0.321
## years_education 0.379231 0.029223 12.977 < 2e-16 ***
## marital_status_num -0.157800 0.139700 -1.130 0.259
## household_role_num -0.949396 0.065195 -14.562 < 2e-16 ***
## hours_per_week 0.027063 0.001651 16.390 < 2e-16 ***
## poly(capital_gain_log, 2)1 163.411208 6.544402 24.970 < 2e-16 ***
## poly(capital_gain_log, 2)2 221.776699 10.975828 20.206 < 2e-16 ***
## poly(capital_loss_log, 2)1 36.189260 2.670990 13.549 < 2e-16 ***
## poly(capital_loss_log, 2)2 27.579139 3.742339 7.369 1.71e-13 ***
## age:race_num -0.002523 0.002816 -0.896 0.370
## race_num:years_education -0.017463 0.012716 -1.373 0.170
## race_num:marital_status_num -0.030278 0.061685 -0.491 0.624
## race_num:household_role_num 0.039380 0.027911 1.411 0.158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 17620 on 26035 degrees of freedom
## AIC: 17650
##
## Number of Fisher Scoring iterations: 7par(mfrow=c(2,2))
plot(complex.fit)
par(mfrow=c(1,1))
plot_model_metrics_by_threshold(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
model_title = "Complex (Interaction Race)"
)
complex <-
print_model_metrics(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
threshold = 0.267,
model_title = "Complex (Interaction Race)"
)## Setting levels: control = 0, case = 1## Setting direction: controls < casesprint(complex)##
##
## Table: Complex (Interaction Race)
## Threshold: 0.267
## AIC: 17650.04
##
## | |Metric | Value|
## |:--------------|:-----------|---------:|
## |Accuracy |Accuracy | 0.8029791|
## |Sensitivity |Sensitivity | 0.8301768|
## |Specificity |Specificity | 0.7942370|
## |Prevalence |Prevalence | 0.2432432|
## |Pos Pred Value |PPV | 0.5646200|
## |Neg Pred Value |NPV | 0.9356921|
## | |AUROC | 0.8967578|Complex Model interaction Occupation
\[ \hat{Y}_{\text{income}} = \beta_0 + (\beta_1 \times \text{age} \times \text{occupation}) + (\beta_2 \times \text{years_education} \times \text{occupation}) + (\beta_3 \times \text{marital_status} \times \text{occupation}) + (\beta_4 \times \text{household_role} \times \text{occupation}) + (\beta_5 \times \text{hours_per_week} \times \text{occupation}) \\ + (\beta_6 \times \text{capital_gain_log} \times \text{occupation}) + (\beta_7 \times \text{capital_loss_log} \times \text{occupation}) \]
# Using Race as interaction except for capital_gain_log and capital_loss_log which is using invested
model_formula <- as.formula("income ~ (age * occupation_num +
years_education +
hours_per_week * occupation_num +
poly(capital_gain_log, 2)+
poly(capital_loss_log, 2))")
complex.fit <- glm(model_formula, data = training_set_LR, family = "binomial")
summary(complex.fit)##
## Call:
## glm(formula = model_formula, family = "binomial", data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.107075 0.293800 -24.190 < 2e-16 ***
## age 0.036934 0.004083 9.046 < 2e-16 ***
## occupation_num -0.248103 0.074809 -3.316 0.000912 ***
## years_education 0.323437 0.007998 40.438 < 2e-16 ***
## hours_per_week 0.025987 0.004159 6.248 4.15e-10 ***
## poly(capital_gain_log, 2)1 167.420136 6.272924 26.689 < 2e-16 ***
## poly(capital_gain_log, 2)2 212.615269 10.497673 20.254 < 2e-16 ***
## poly(capital_loss_log, 2)1 41.450654 2.413631 17.174 < 2e-16 ***
## poly(capital_loss_log, 2)2 24.829645 3.419501 7.261 3.84e-13 ***
## age:occupation_num 0.001703 0.001081 1.575 0.115223
## occupation_num:hours_per_week 0.004363 0.001128 3.866 0.000111 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 20858 on 26039 degrees of freedom
## AIC: 20880
##
## Number of Fisher Scoring iterations: 7par(mfrow=c(2,2))
plot(complex.fit)
par(mfrow=c(1,1))
plot_model_metrics_by_threshold(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
model_title = "Complex (Interaction Occupation)"
)
complex <-
print_model_metrics(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
threshold = 0.267,
model_title = "Complex (Interaction Occupation)"
)## Setting levels: control = 0, case = 1## Setting direction: controls < casesprint(complex)##
##
## Table: Complex (Interaction Occupation)
## Threshold: 0.267
## AIC: 20879.64
##
## | |Metric | Value|
## |:--------------|:-----------|---------:|
## |Accuracy |Accuracy | 0.7820946|
## |Sensitivity |Sensitivity | 0.6862374|
## |Specificity |Specificity | 0.8129058|
## |Prevalence |Prevalence | 0.2432432|
## |Pos Pred Value |PPV | 0.5410652|
## |Neg Pred Value |NPV | 0.8896291|
## | |AUROC | 0.8431840|Complex Model interaction Household Role
model_formula <- as.formula("income ~ (age +
years_education +
marital_status_num +
hours_per_week +
capital_gain_log +
capital_loss_log) * household_role_num")
complex.fit <- glm(model_formula, data = training_set_LR, family = "binomial")
summary(complex.fit)##
## Call:
## glm(formula = model_formula, family = "binomial", data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.390042 0.474232 -7.148 8.77e-13 ***
## age 0.013299 0.002884 4.611 4.00e-06 ***
## years_education 0.344671 0.015311 22.512 < 2e-16 ***
## marital_status_num -0.628840 0.183475 -3.427 0.000609 ***
## hours_per_week 0.020920 0.002982 7.015 2.30e-12 ***
## capital_gain_log 0.129349 0.012391 10.439 < 2e-16 ***
## capital_loss_log 0.133400 0.018989 7.025 2.14e-12 ***
## household_role_num -1.492908 0.181668 -8.218 < 2e-16 ***
## age:household_role_num 0.004082 0.001395 2.926 0.003432 **
## years_education:household_role_num 0.001374 0.007943 0.173 0.862707
## marital_status_num:household_role_num 0.131390 0.055086 2.385 0.017071 *
## hours_per_week:household_role_num 0.003892 0.001448 2.687 0.007207 **
## capital_gain_log:household_role_num 0.037282 0.005605 6.652 2.90e-11 ***
## capital_loss_log:household_role_num 0.010855 0.009361 1.160 0.246222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 18567 on 26036 degrees of freedom
## AIC: 18595
##
## Number of Fisher Scoring iterations: 7par(mfrow=c(2,2))
plot(complex.fit)
par(mfrow=c(1,1))
plot_model_metrics_by_threshold(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
model_title = "Complex (Interaction Household Role)"
)
complex <-
print_model_metrics(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
threshold = 0.267,
model_title = "Complex (Interaction Household Role)"
)## Setting levels: control = 0, case = 1## Setting direction: controls < casesprint(complex)##
##
## Table: Complex (Interaction Household Role)
## Threshold: 0.267
## AIC: 18594.90
##
## | |Metric | Value|
## |:--------------|:-----------|---------:|
## |Accuracy |Accuracy | 0.7919226|
## |Sensitivity |Sensitivity | 0.8428030|
## |Specificity |Specificity | 0.7755682|
## |Prevalence |Prevalence | 0.2432432|
## |Pos Pred Value |PPV | 0.5469070|
## |Neg Pred Value |NPV | 0.9388357|
## | |AUROC | 0.8883234|Complex Model interaction Household Role with poly
model_formula <- as.formula("income ~ (age +
years_education +
hours_per_week +
poly(capital_gain_log, 2)+
poly(capital_loss_log, 2)) * household_role_num")
complex.fit <- glm(model_formula, data = training_set_LR, family = "binomial")
summary(complex.fit)##
## Call:
## glm(formula = model_formula, family = "binomial", data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -3.977761 0.267292 -14.882
## age 0.011150 0.003000 3.717
## years_education 0.328024 0.015813 20.744
## hours_per_week 0.018934 0.003084 6.140
## poly(capital_gain_log, 2)1 174.748578 12.266332 14.246
## poly(capital_gain_log, 2)2 247.691043 19.386861 12.776
## poly(capital_loss_log, 2)1 34.280589 5.164713 6.637
## poly(capital_loss_log, 2)2 26.633920 8.393745 3.173
## household_role_num -1.370562 0.131753 -10.403
## age:household_role_num 0.005790 0.001470 3.937
## years_education:household_role_num 0.004108 0.008338 0.493
## hours_per_week:household_role_num 0.004836 0.001527 3.168
## poly(capital_gain_log, 2)1:household_role_num -4.107000 4.283546 -0.959
## poly(capital_gain_log, 2)2:household_role_num -11.837259 7.302662 -1.621
## poly(capital_loss_log, 2)1:household_role_num 1.130830 2.680793 0.422
## poly(capital_loss_log, 2)2:household_role_num 0.027641 2.886015 0.010
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## age 0.000202 ***
## years_education < 2e-16 ***
## hours_per_week 8.24e-10 ***
## poly(capital_gain_log, 2)1 < 2e-16 ***
## poly(capital_gain_log, 2)2 < 2e-16 ***
## poly(capital_loss_log, 2)1 3.19e-11 ***
## poly(capital_loss_log, 2)2 0.001508 **
## household_role_num < 2e-16 ***
## age:household_role_num 8.24e-05 ***
## years_education:household_role_num 0.622245
## hours_per_week:household_role_num 0.001537 **
## poly(capital_gain_log, 2)1:household_role_num 0.337667
## poly(capital_gain_log, 2)2:household_role_num 0.105028
## poly(capital_loss_log, 2)1:household_role_num 0.673152
## poly(capital_loss_log, 2)2:household_role_num 0.992358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 17640 on 26034 degrees of freedom
## AIC: 17672
##
## Number of Fisher Scoring iterations: 7par(mfrow=c(2,2))
plot(complex.fit)
par(mfrow=c(1,1))
plot_model_metrics_by_threshold(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
model_title = "Complex (Interaction Household Role and polynomial)"
)
complex6 <-
print_model_metrics(
model = complex.fit,
test_data = testing_set_LR,
response_var = "income",
pos_value = "1",
threshold = 0.267,
model_title = "Complex (Interaction Household Role and polynomial)"
)## Setting levels: control = 0, case = 1## Setting direction: controls < casesprint(complex6)##
##
## Table: Complex (Interaction Household Role and polynomial)
## Threshold: 0.267
## AIC: 17671.66
##
## | |Metric | Value|
## |:--------------|:-----------|---------:|
## |Accuracy |Accuracy | 0.7994472|
## |Sensitivity |Sensitivity | 0.8383838|
## |Specificity |Specificity | 0.7869318|
## |Prevalence |Prevalence | 0.2432432|
## |Pos Pred Value |PPV | 0.5584525|
## |Neg Pred Value |NPV | 0.9380745|
## | |AUROC | 0.8960806|Objective two Models part 2
Test with interactions added on suspected variables with higher multicollinearity using the One-Hot_encoded data. Some interaction showed significance, but the model performed equal to model 3 above.
################## glm test with influential outliers removed ##############################
cat("\n\n----------------interactions--------------\n\n")##
##
## ----------------interactions--------------interactions_formula <- as.formula(income ~ +age + workclass_num + education_level_num + years_education + marital_status_num+
occupation_num + household_role_num + race_num + gender_num + capital_gain_log + capital_loss_log + hours_per_week +
native_region_num + capital_gain_log:capital_loss_log +
education_level_num:years_education +workclass_num:occupation_num)
interactions_formula## income ~ +age + workclass_num + education_level_num + years_education +
## marital_status_num + occupation_num + household_role_num +
## race_num + gender_num + capital_gain_log + capital_loss_log +
## hours_per_week + native_region_num + capital_gain_log:capital_loss_log +
## education_level_num:years_education + workclass_num:occupation_numinteractions_formula2 <- as.formula(income ~ +age + workclass_num + similar_pop_count + education_level_num + years_education + marital_status_num+
occupation_num + household_role_num + race_num + gender_num + capital_gain + capital_loss + hours_per_week +
native_region_num + invested_num + capital_gain:capital_loss +
education_level_num:years_education +workclass_num:occupation_num)
# Fit a logistic regression model
model6 <- glm(interactions_formula, data = training_set_LR, family = binomial)
# Confidence interval
# confint(model3)
# Summarize the final selected model
summary(model6)##
## Call:
## glm(formula = interactions_formula, family = binomial, data = training_set_LR)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.6414134 0.3660397 -7.216 5.35e-13 ***
## age 0.0224867 0.0015816 14.218 < 2e-16 ***
## workclass_num -0.3376674 0.0358582 -9.417 < 2e-16 ***
## education_level_num 0.0227006 0.0527910 0.430 0.6672
## years_education 0.3333134 0.0200845 16.596 < 2e-16 ***
## marital_status_num -0.2040444 0.0333119 -6.125 9.05e-10 ***
## occupation_num -0.4746475 0.0450867 -10.527 < 2e-16 ***
## household_role_num -0.8647059 0.0223604 -38.671 < 2e-16 ***
## race_num -0.0518539 0.0320103 -1.620 0.1053
## gender_num -0.1036843 0.0579356 -1.790 0.0735 .
## capital_gain_log 0.2005975 0.0069272 28.958 < 2e-16 ***
## capital_loss_log 0.1516110 0.0099767 15.197 < 2e-16 ***
## hours_per_week 0.0273096 0.0016530 16.521 < 2e-16 ***
## native_region_num 0.0180578 0.0311560 0.580 0.5622
## capital_gain_log:capital_loss_log -0.2709513 3.8179239 -0.071 0.9434
## education_level_num:years_education -0.0009126 0.0044164 -0.207 0.8363
## workclass_num:occupation_num 0.0802809 0.0089069 9.013 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28723 on 26049 degrees of freedom
## Residual deviance: 18495 on 26033 degrees of freedom
## AIC: 18529
##
## Number of Fisher Scoring iterations: 9# Check for multicollinearity
# vif(model6)
# Perform Hosmer-Lemeshow goodness of fit test
# Note: A p-value less than 0.05 typically suggests that the model does not fit the data well.
hoslem.test(training_set_LR$income, fitted(model6))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: training_set_LR$income, fitted(model6)
## X-squared = 50.812, df = 8, p-value = 2.852e-08# Make predictions
probabilities <- model6 %>% predict(testing_set_LR, type = "response")
predicted.classes <- ifelse(probabilities > 0.28, "1", "0")
# Model accuracy
accuracy <- mean(predicted.classes==testing_set_LR$income)
# confusion matrix
tb <- table(predicted.classes, testing_set_LR$income)
tb##
## predicted.classes 0 1
## 0 3971 316
## 1 957 1268# Calculate Sensitivity (True Positive Rate)
sensitivity <- tb[2, 2] / (tb[2, 2] + tb[1, 2])
# Calculate Specificity (True Negative Rate)
specificity <- tb[1, 1] / (tb[1, 1] + tb[2, 1])
# Calculate Prevalence
prevalence <- (tb[1, 2] + tb[2, 2]) / sum(tb)
# Calculate PPV (Positive Predictive Value)
ppv <- tb[2, 2] / (tb[2, 2] + tb[2, 1])
# Calculate NPV (Negative Predictive Value)
npv <- tb[1, 1] / (tb[1, 1] + tb[1, 2])
# Calculate AUROC
roc_obj <- roc(testing_set_LR$income, probabilities)## Setting levels: control = 0, case = 1## Setting direction: controls < casesauroc <- auc(roc_obj)
# Print the metrics
cat("accuracy:", accuracy, "\n")## accuracy: 0.8045147cat("Sensitivity:", sensitivity, "\n")## Sensitivity: 0.8005051cat("Specificity:", specificity, "\n")## Specificity: 0.8058036cat("Prevalence:", prevalence, "\n")## Prevalence: 0.2432432cat("PPV:", ppv, "\n")## PPV: 0.5698876cat("NPV:", npv, "\n")## NPV: 0.9262888cat("AUROC:", auroc, "\n")## AUROC: 0.8880226lda full
library(caret)
# change the income value names to work with glmnet (greater than sign will not run)
training_set2 <- training_set
training_set2$income <- gsub('<=50K', 'less_than_50K', training_set2$income)
training_set2$income <- gsub('>50K', 'greater_than_50K', training_set2$income)
testing_set2 <- testing_set
testing_set2$income <- gsub('<=50K', 'less_than_50K', testing_set2$income)
testing_set2$income <- gsub('>50K', 'greater_than_50K', testing_set2$income)
levels(training_set2$income)## NULLfitControl<-trainControl(method="repeatedcv",number=2,repeats=1,classProbs=TRUE, summaryFunction=mnLogLoss)
set.seed(1234)
# Convert 'income' to a factor
training_set2$income <- factor(training_set2$income, levels = c('greater_than_50K', 'less_than_50K'))
testing_set2$income <- factor(testing_set2$income, levels = c('greater_than_50K', 'less_than_50K'))
# Fit a logistic regression model
logreg_model <- train(income ~ ., data = training_set2,
method = "lda",
trControl = fitControl,
metric = "logLoss")
# Compute predicted probabilities on the training data
logreg_predictions <- predict(logreg_model, testing_set2, type = "prob")[, 'less_than_50K']
threshold <- 0.78
# Create factor predictions based on a threshold
logreg_preds <- factor(ifelse(logreg_predictions > threshold, 'less_than_50K', 'greater_than_50K' ),
levels = c( 'less_than_50K', 'greater_than_50K' ))
# Get the confusion matrix
confusionMatrix(data = logreg_preds, reference = testing_set2$income)## Warning in confusionMatrix.default(data = logreg_preds, reference =
## testing_set2$income): Levels are not in the same order for reference and data.
## Refactoring data to match.## Confusion Matrix and Statistics
##
## Reference
## Prediction greater_than_50K less_than_50K
## greater_than_50K 1285 967
## less_than_50K 318 3942
##
## Accuracy : 0.8027
## 95% CI : (0.7928, 0.8123)
## No Information Rate : 0.7538
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5321
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.8016
## Specificity : 0.8030
## Pos Pred Value : 0.5706
## Neg Pred Value : 0.9254
## Prevalence : 0.2462
## Detection Rate : 0.1973
## Detection Prevalence : 0.3458
## Balanced Accuracy : 0.8023
##
## 'Positive' Class : greater_than_50K
## library(ROCR)
roc_obj <- roc(testing_set2$income, logreg_predictions)## Setting levels: control = greater_than_50K, case = less_than_50K## Setting direction: controls < casesauroc <- auc(roc_obj)
cat("AUROC:", auroc, "\n")## AUROC: 0.8892327Non-Parametric Model testing.
Classification and Regression Trees (CART)
Source: https://carpentries-incubator.github.io/r-ml-tabular-data/04-Decision-Forests/index.html
set.seed(1234)
library(rpart)
# install.packages("rpart.plot")
library(rpart.plot)
# create a decision tree model
rwtree <- rpart(new_formula2, data = training_set_out, method = "class", cp = 1e-10) # CP Complexity- lower is more complex
rpart.plot(rwtree)## Warning: labs do not fit even at cex 0.15, there may be some overplotting
# summary(rwtree)
# Predict class probabilities
rwtree.pred.prob <- predict(rwtree, newdata = testing_set_out, type = 'prob')
# Apply threshold to get predicted classes
threshold <- 0.22
rwtree.pred <- ifelse(rwtree.pred.prob[, ">50K"] > threshold, ">50K", "<=50K")
# Confusion matrix
tb5 <- table(rwtree.pred, testing_set_out$income)
tb5##
## rwtree.pred <=50K >50K
## <=50K 3991 252
## >50K 896 1370rwtreeErrors <- rwtree.pred.prob - ifelse(testing_set_out$income == ">50K", 1, 0)
rwtreeRMSE <- sqrt(mean(rwtreeErrors^2))
# Model accuracy
accuracy <- mean(rwtree.pred == testing_set_out$income)
# Calculate Sensitivity (True Positive Rate)
sensitivity <- tb5[2, 2] / (tb5[2, 2] + tb5[1, 2])
# Calculate Specificity (True Negative Rate)
specificity <- tb5[1, 1] / (tb5[1, 1] + tb5[2, 1])
# Calculate Prevalence
prevalence <- (tb5[1, 2] + tb5[2, 2]) / sum(tb5)
# Calculate PPV (Positive Predictive Value)
ppv <- tb5[2, 2] / (tb5[2, 2] + tb5[2, 1])
# Calculate NPV (Negative Predictive Value)
npv <- tb5[1, 1] / (tb5[1, 1] + tb5[1, 2])
# Assuming the positive class is '>50K'
positive_class_probs <- rwtree.pred.prob[, ">50K"]
# Calculate AUROC using the vector of probabilities for the positive class
roc_obj <- roc(testing_set_out$income, positive_class_probs)## Setting levels: control = <=50K, case = >50K## Setting direction: controls < casesauroc <- auc(roc_obj)
# Print the metrics
cat("Accuracy:", accuracy, "\n")## Accuracy: 0.8236288cat("Sensitivity:", sensitivity, "\n")## Sensitivity: 0.8446363cat("Specificity:", specificity, "\n")## Specificity: 0.8166564cat("Prevalence:", prevalence, "\n")## Prevalence: 0.2491934cat("PPV:", ppv, "\n")## PPV: 0.6045896cat("NPV:", npv, "\n")## NPV: 0.9406081cat("AUROC:", auroc, "\n")## AUROC: 0.9040478cat("RMSE:", rwtreeRMSE, "\n")## RMSE: 0.6436333Random forest (RF) improves predictive accuracy by generating a large number of bootstrapped trees.
Sources: https://carpentries-incubator.github.io/r-ml-tabular-data/04-Decision-Forests/index.html
- MeanDecreaseAccuracy measures how much the model’s accuracy decreases when a specific feature is excluded. Higher values indicate that excluding that feature would significantly impact prediction accuracy.
- MeanDecreaseGini assesses how each feature contributes to the homogeneity of nodes and leaves in the random forest.A higher value suggests that excluding that feature would lead to less pure (more impure) nodes.
- Compare the importance rankings between the two metrics. If a feature is consistently high in both panels, it is likely crucial.
- Plot - Out-of-Bag (OOB) Error Line (Green, Dotted), Test Error Line (Red, Dashed): both models plateau quickly and are no longer viable after 200 trees.
set.seed(1234)
library(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:ggplot2':
##
## margin# Train the random forest model
rf3t <- randomForest(income ~ ., data = training_set_out, ntree = 300, keep.forest=TRUE, importance=TRUE,test=testing_set_out$income)
rf3t##
## Call:
## randomForest(formula = income ~ ., data = training_set_out, ntree = 300, keep.forest = TRUE, importance = TRUE, test = testing_set_out$income)
## Type of random forest: classification
## Number of trees: 300
## No. of variables tried at each split: 3
##
## OOB estimate of error rate: 13.29%
## Confusion matrix:
## <=50K >50K class.error
## <=50K 18670 1164 0.0586871
## >50K 2296 3908 0.3700838plot(rf3t)
# Visually check variable importance.
importance(rf3t)## <=50K >50K MeanDecreaseAccuracy MeanDecreaseGini
## age 3.210170 75.223704 64.794813 887.91532
## workclass 30.129097 14.777020 36.316584 302.91282
## education_level 13.311274 23.637330 23.757605 481.80628
## years_education 16.841759 19.349687 28.842830 592.88945
## marital_status 7.122960 19.405890 31.343596 789.41471
## occupation 8.156144 79.763747 70.268402 489.54197
## household_role 7.199914 82.646946 36.246245 947.50828
## race 8.255063 9.383508 13.393072 119.72564
## gender 14.023378 7.979982 23.217124 99.88164
## hours_per_week 4.372741 44.535055 41.596057 530.63468
## native_region 9.775015 -2.721376 6.264715 116.34315
## capital_gain_log 120.685600 164.993010 162.828528 993.68817
## capital_loss_log 49.129691 74.100736 75.119828 293.56159varImpPlot(rf3t)
# Predict class probabilities
rf3t.pred.prob <- predict(rf3t, newdata = testing_set_out, type = 'prob')
# Apply threshold to get predicted classes
threshold <- 0.2
rf3t.pred <- ifelse(rf3t.pred.prob[, ">50K"] > threshold, ">50K", "<=50K")
# Confusion matrix
tb3t <- table(rf3t.pred, testing_set_out$income)
tb3t##
## rf3t.pred <=50K >50K
## <=50K 4128 323
## >50K 759 1299# Get the RMSE -not sure how useful this is because it is categorical
rfErrors <- rf3t.pred.prob - ifelse(testing_set_out$income == ">50K", 1, 0)
rfRMSE <- sqrt(mean(rfErrors^2))
rfRMSE## [1] 0.6598331# Model accuracy
accuracy <- mean(rf3t.pred == testing_set_out$income)
# Calculate Sensitivity (True Positive Rate)
sensitivity <- tb3t[2, 2] / (tb3t[2, 2] + tb3t[1, 2])
# Calculate Specificity (True Negative Rate)
specificity <- tb3t[1, 1] / (tb3t[1, 1] + tb3t[2, 1])
# Calculate Prevalence
prevalence <- (tb3t[1, 2] + tb3t[2, 2]) / sum(tb3t)
# Calculate PPV (Positive Predictive Value)
ppv <- tb3t[2, 2] / (tb3t[2, 2] + tb3t[2, 1])
# Calculate NPV (Negative Predictive Value)
npv <- tb3t[1, 1] / (tb3t[1, 1] + tb3t[1, 2])
# Assuming the positive class is '>50K'
positive_class_probs <- rf3t.pred.prob[, ">50K"]
# Calculate AUROC using the vector of probabilities for the positive class
roc_obj <- roc(testing_set_out$income, positive_class_probs)## Setting levels: control = <=50K, case = >50K## Setting direction: controls < casesauroc <- auc(roc_obj)
# Print the metrics
cat("Accuracy:", accuracy, "\n")## Accuracy: 0.8337686cat("Sensitivity:", sensitivity, "\n")## Sensitivity: 0.8008631cat("Specificity:", specificity, "\n")## Specificity: 0.84469cat("Prevalence:", prevalence, "\n")## Prevalence: 0.2491934cat("PPV:", ppv, "\n")## PPV: 0.6311953cat("NPV:", npv, "\n")## NPV: 0.927432cat("AUROC:", auroc, "\n")## AUROC: 0.9016705cat("RMSE:", rfRMSE, "\n")## RMSE: 0.6598331Random Forest reduced model
set.seed(1234)
cat("\n---------------reduced model-------------\n")##
## ---------------reduced model-------------new_formula2 <- as.formula(income ~ +age + years_education + capital_gain_log + capital_loss_log +
marital_status+ occupation + household_role + hours_per_week)
# Train the reduced random forest model
rf3 <- randomForest(new_formula2, data = training_set_out, ntree = 300)
rf3##
## Call:
## randomForest(formula = new_formula2, data = training_set_out, ntree = 300)
## Type of random forest: classification
## Number of trees: 300
## No. of variables tried at each split: 2
##
## OOB estimate of error rate: 13.3%
## Confusion matrix:
## <=50K >50K class.error
## <=50K 18777 1057 0.05329233
## >50K 2407 3797 0.38797550plot(rf3)
# Visually check variable importance.
importance(rf3)## MeanDecreaseGini
## age 664.5262
## years_education 822.9584
## capital_gain_log 1041.3301
## capital_loss_log 300.5223
## marital_status 665.2560
## occupation 567.3563
## household_role 1082.5898
## hours_per_week 425.7741varImpPlot(rf3)
# Predict class probabilities
rf3.pred.prob <- predict(rf3, newdata = testing_set_out, type = 'prob')
# Apply threshold to get predicted classes
threshold <- 0.13
rf3.pred <- ifelse(rf3.pred.prob[, ">50K"] > threshold, ">50K", "<=50K")
# Confusion matrix
tb3 <- table(rf3.pred, testing_set_out$income)
tb3##
## rf3.pred <=50K >50K
## <=50K 4057 302
## >50K 830 1320# Get the RMSE -not sure how useful this is because it is categorical
rfErrors <- rf3.pred.prob - ifelse(testing_set_out$income == ">50K", 1, 0)
rfRMSE <- sqrt(mean(rfErrors^2))
# Model accuracy
accuracy <- mean(rf3.pred == testing_set_out$income)
# Calculate Sensitivity (True Positive Rate)
sensitivity <- tb3[2, 2] / (tb3[2, 2] + tb3[1, 2])
# Calculate Specificity (True Negative Rate)
specificity <- tb3[1, 1] / (tb3[1, 1] + tb3[2, 1])
# Calculate Prevalence
prevalence <- (tb3[1, 2] + tb3[2, 2]) / sum(tb3)
# Calculate PPV (Positive Predictive Value)
ppv <- tb3[2, 2] / (tb3[2, 2] + tb3[2, 1])
# Calculate NPV (Negative Predictive Value)
npv <- tb3[1, 1] / (tb3[1, 1] + tb3[1, 2])
# Assuming the positive class is '>50K'
positive_class_probs <- rf3.pred.prob[, ">50K"]
# Calculate AUROC using the vector of probabilities for the positive class
roc_obj <- roc(testing_set_out$income, positive_class_probs)## Setting levels: control = <=50K, case = >50K## Setting direction: controls < casesauroc <- auc(roc_obj)
# Print the metrics
cat("Accuracy:", accuracy, "\n")## Accuracy: 0.826087cat("Sensitivity:", sensitivity, "\n")## Sensitivity: 0.8138101cat("Specificity:", specificity, "\n")## Specificity: 0.8301617cat("Prevalence:", prevalence, "\n")## Prevalence: 0.2491934cat("PPV:", ppv, "\n")## PPV: 0.6139535cat("NPV:", npv, "\n")## NPV: 0.9307181cat("AUROC:", auroc, "\n")## AUROC: 0.8953958cat("RMSE:", rfRMSE, "\n")## RMSE: 0.6701497Support vector machine (SVM) to predict the income level
library(kernlab)##
## Attaching package: 'kernlab'## The following object is masked from 'package:purrr':
##
## cross## The following object is masked from 'package:ggplot2':
##
## alphalibrary(caret)
set.seed(1234)
# Train the SVM model
svm4 <- ksvm(income ~ ., data = training_set_out, )
# Get decision values
svm4.pred.prob <- predict(svm4, newdata = testing_set_out, type = 'decision')
# Apply threshold to decision values to get predicted classes
threshold <- -.9 # Adjust this threshold (not normal for SVM)
svm4.pred <- ifelse(svm4.pred.prob > threshold, ">50K", "<=50K")
# Confusion matrix
tb4 <- table(svm4.pred, testing_set_out$income)
tb4##
## svm4.pred <=50K >50K
## <=50K 4144 351
## >50K 743 1271# Calculate accuracy and other metrics
accuracy <- mean(svm4.pred == testing_set_out$income)
sensitivity <- tb4[2, 2] / (tb4[2, 2] + tb4[1, 2])
specificity <- tb4[1, 1] / (tb4[1, 1] + tb4[2, 1])
prevalence <- (tb4[1, 2] + tb4[2, 2]) / sum(tb4)
ppv <- tb4[2, 2] / (tb4[2, 2] + tb4[2, 1])
npv <- tb4[1, 1] / (tb4[1, 1] + tb4[1, 2])
# Calculate AUROC using the decision values
roc_obj <- roc(testing_set_out$income, svm4.pred.prob)## Setting levels: control = <=50K, case = >50K## Warning in roc.default(testing_set_out$income, svm4.pred.prob): Deprecated use
## a matrix as predictor. Unexpected results may be produced, please pass a
## numeric vector.## Setting direction: controls < casesauroc <- auc(roc_obj)
# Print the metrics
cat("Accuracy:", accuracy, "\n")## Accuracy: 0.831925cat("Sensitivity:", sensitivity, "\n")## Sensitivity: 0.7836005cat("Specificity:", specificity, "\n")## Specificity: 0.847964cat("Prevalence:", prevalence, "\n")## Prevalence: 0.2491934cat("PPV:", ppv, "\n")## PPV: 0.6310824cat("NPV:", npv, "\n")## NPV: 0.9219132cat("AUROC:", auroc, "\n")## AUROC: 0.8920914- Accuracy: This tells you the overall correctness of the model, but it doesn’t account for the class imbalance or the costs of different types of errors.
- Sensitivity (True Positive Rate): Indicates how well the model predicts the positive class, in this case, those earning over 50K. It’s important if you don’t want to miss individuals who are actually earning more than 50K. *Specificity (True Negative Rate): Shows how well the model predicts the negative class, those earning under 50K. It’s important if you want to minimize false alarms, where people are incorrectly predicted to earn more than 50K.
- Prevalence: This is the actual proportion of the positive class in your dataset. It doesn’t help with prediction but gives context about the dataset.
- PPV (Positive Predictive Value): Tells you how likely it is that a person predicted to earn over 50K actually does. This is useful if the cost of falsely identifying someone as earning over 50K is high.
- NPV (Negative Predictive Value): Indicates the likelihood that a person predicted to earn under 50K actually earns less than that. It’s useful when it’s important not to misclassify those who earn under 50K.
- AUROC (Area Under the Receiver Operating Characteristic curve): This metric is useful for evaluating the model’s ability to discriminate between the two classes across all thresholds. A high AUROC indicates a model that is good at ranking predictions rather than making a hard cutoff.
ROC Curve
Sources: https://www.statology.org/interpret-roc-curve/
prob <- predict(model4, testing_set, type = 'response') # reduced
# create a prediction object
pr <- prediction(prob, testing_set$income)
prf <- performance(pr, measure = "tpr", x.measure = "fpr")
# create a data frame for TP and FP rates
dd <- data.frame(FP = prf@x.values[[1]], TP = prf@y.values[[1]])
# complex LR
prob5 <- predict(model6, testing_set_LR, type = 'response') #interactions
# create a prediction object
pr5 <- prediction(prob5, testing_set_LR$income)
prf5 <- performance(pr5, measure = "tpr", x.measure = "fpr")
# create a data frame for TP and FP rates
dd5 <- data.frame(FP = prf5@x.values[[1]], TP = prf5@y.values[[1]])
# NN
pr1 <- prediction(nn1.pred, testing_set_out$income)
prf1 <- performance(pr1, measure = "tpr", x.measure = "fpr")
dd1 <- data.frame(FP = prf1@x.values[[1]], TP = prf1@y.values[[1]])
# CART
pr2 <- prediction(tree2.pred.prob[,2], testing_set_out$income)
prf2 <- performance(pr2, measure = "tpr", x.measure = "fpr")
dd2 <- data.frame(FP = prf2@x.values[[1]], TP = prf2@y.values[[1]])
# RF
pr3 <- prediction(rf3.pred.prob[,2], testing_set_out$income)
prf3 <- performance(pr3, measure = "tpr", x.measure = "fpr")
dd3 <- data.frame(FP = prf3@x.values[[1]], TP = prf3@y.values[[1]])
# SVM
pr4 <- prediction(svm4.pred.prob, testing_set_out$income)
prf4 <- performance(pr4, measure = "tpr", x.measure = "fpr")
dd4 <- data.frame(FP = prf4@x.values[[1]], TP = prf4@y.values[[1]])
# Create a prediction object for the lda model (logreg_predictions)
pr6 <- prediction(logreg_predictions, testing_set2$income)
# Calculate the True Positive Rate (TPR) and False Positive Rate (FPR)
prf6 <- performance(pr6, measure = "tpr", x.measure = "fpr")
dd6 <- data.frame(FP = prf6@x.values[[1]], TP = prf6@y.values[[1]])
# plot ROC curve for logistic regression
g <- ggplot() +
geom_line(data = dd, aes(x = FP, y = TP, color = 'Logistic Regression')) +
geom_line(data = dd5, aes(x = FP, y = TP, color = 'Complex Logistic Regression')) +
geom_line(data = dd1, aes(x = FP, y = TP, color = 'Neural Networks')) +
geom_line(data = dd2, aes(x = FP, y = TP, color = 'CART')) +
geom_line(data = dd3, aes(x = FP, y = TP, color = 'Random Forest')) +
geom_line(data = dd4, aes(x = FP, y = TP, color = 'Support Vector Machine')) +
geom_line(data = dd6, aes(x = FP, y = TP, color = 'Linear Discriminant Analysis')) +
geom_segment(aes(x = 0, xend = 1, y = 0, yend = 1)) +
ggtitle('ROC Curve') +
labs(x = 'False Positive Rate', y = 'True Positive Rate')
g + scale_colour_manual(name = 'Classifier', values = c('Logistic Regression'='#E69F00',
'Complex Logistic Regression'='red',
'Neural Networks'='blue', 'CART'='#009E73',
'Random Forest'='#D55E00', 'Support Vector Machine'='#0072B2',
'Linear Discriminant Analysis' = 'green'))
# AUC
auc <- rbind(performance(pr, measure = 'auc')@y.values[[1]],
performance(pr1, measure = 'auc')@y.values[[1]],
performance(pr2, measure = 'auc')@y.values[[1]],
performance(pr3, measure = 'auc')@y.values[[1]],
performance(pr4, measure = 'auc')@y.values[[1]],
performance(pr5, measure = 'auc')@y.values[[1]],
performance(pr6, measure = 'auc')@y.values[[1]])
rownames(auc) <- (c('Logistic Regression', 'Neural Networks', 'CART',
'Random Forest', 'Support Vector Machine',
'Complex Logistic Regression', 'Linear Discriminant Analysis'))
colnames(auc) <- 'Area Under ROC Curve'
round(auc, 4)## Area Under ROC Curve
## Logistic Regression 0.8955
## Neural Networks 0.9010
## CART 0.9040
## Random Forest 0.8954
## Support Vector Machine 0.8921
## Complex Logistic Regression 0.8880
## Linear Discriminant Analysis 0.8892